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Sashittal P, Zhang RY, Law BK, Strzalkowski A, Schmidt H, Bolondi A, Chan MM, Raphael BJ. Inferring cell differentiation maps from lineage tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.09.611835. [PMID: 39314473 PMCID: PMC11419031 DOI: 10.1101/2024.09.09.611835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
During development, mulitpotent cells differentiate through a hierarchy of increasingly restricted progenitor cell types until they realize specialized cell types. A cell differentiation map describes this hierarchy, and inferring these maps is an active area of research spanning traditional single marker lineage studies to data-driven trajectory inference methods on single-cell RNA-seq data. Recent high-throughput lineage tracing technologies profile lineages and cell types at scale, but current methods to infer cell differentiation maps from these data rely on simple models with restrictive assumptions about the developmental process. We introduce a mathematical framework for cell differentiation maps based on the concept of potency, and develop an algorithm, Carta , that infers an optimal cell differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the trade-off between the complexity of the cell differentiation map and the number of unobserved cell type transitions on the lineage tree. We show that Carta more accurately infers cell differentiation maps on both simulated and real data compared to existing methods. In models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods including convergent differentiation of specialized cell types, progenitor differentiation dynamics, and the refinement of routes of differentiation via new intermediate progenitors. Code availability Carta software is available at https://github.com/raphael-group/CARTA.
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2
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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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3
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Liu F, Zhang X, Yang Y. Simulation of CRISPR-Cas9 editing on evolving barcode and accuracy of lineage tracing. Sci Rep 2024; 14:19213. [PMID: 39160220 PMCID: PMC11333585 DOI: 10.1038/s41598-024-70154-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 08/13/2024] [Indexed: 08/21/2024] Open
Abstract
We designed a simulation program that mimics the CRISPR-Cas9 editing on evolving barcode and double strand break repair procedure along with cell divisions. Emerging barcode mutations tend to build upon previously existing mutations, occurring sequentially with each generation. This process results in a unique mutation profile in each cell. We sample the barcodes in leaf cells and reconstruct the lineage, comparing it to the original lineage tree to test algorithm accuracy under different parameter settings. Our computational simulations validate the reasonable assumptions deduced from experimental observations, emphasizing that factors such as sampling size, barcode length, multiple barcodes, indel probabilities, and Cas9 activity are critical for accurate and successful lineage tracing. Among the many factors we found that sampling size and indel probabilities are two major ones that affect lineage tracing accuracy. Large segment deletions in early generations could greatly impact lineage accuracy. These simulation results offer insightful recommendations for enhancing the design and analysis of Cas9-mediated molecular barcodes in actual experiments.
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Affiliation(s)
- Fengshuo Liu
- Graduate Program in Cancer and Cell Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Xiang Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Yipeng Yang
- Department of Mathematics and Statistics, University of Houston - Clear Lake, 2700 Bay Area Blvd, Houston, TX, 77058, USA.
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4
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Bonham-Carter B, Schiebinger G. Cellular proliferation biases clonal lineage tracing and trajectory inference. Bioinformatics 2024; 40:btae483. [PMID: 39102821 PMCID: PMC11316616 DOI: 10.1093/bioinformatics/btae483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 03/28/2024] [Accepted: 07/30/2024] [Indexed: 08/07/2024] Open
Abstract
MOTIVATION Lineage tracing and trajectory inference from single-cell RNA-sequencing data hold tremendous potential for uncovering the genetic programs driving development and disease. Single cell datasets are thought to provide an unbiased view on the diverse cellular architecture of tissues. Sampling bias, however, can skew single cell datasets away from the cellular composition they are meant to represent. RESULTS We demonstrate a novel form of sampling bias, caused by a statistical phenomenon related to repeated sampling from a growing, heterogeneous population. Relative growth rates of cells influence the probability that they will be sampled in clones observed across multiple time points. We support our probabilistic derivations with a simulation study and an analysis of a real time-course of T-cell development. We find that this bias can impact fate probability predictions, and we explore how to develop trajectory inference methods which are robust to this bias. AVAILABILITY AND IMPLEMENTATION Source code for the simulated datasets and to create the figures in this manuscript is freely available in python at https://github.com/rbonhamcarter/simulate-clones. A python implementation of the extension of the LineageOT method is freely available at https://github.com/rbonhamcarter/LineageOT/tree/multi-time-clones.
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Affiliation(s)
- Becca Bonham-Carter
- Department of Mathematics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Geoffrey Schiebinger
- Department of Mathematics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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5
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Li H, Zhuang Y, Zhang B, Xu X, Liu B. Application of Lineage Tracing in Central Nervous System Development and Regeneration. Mol Biotechnol 2024; 66:1552-1562. [PMID: 37335434 PMCID: PMC11217125 DOI: 10.1007/s12033-023-00769-0] [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: 12/07/2022] [Accepted: 05/09/2023] [Indexed: 06/21/2023]
Abstract
The central nervous system (CNS) is a complicated neural network. The origin and evolution of functional neurons and glia cells remain unclear, as do the cellular alterations that occur during the course of cerebral disease rehabilitation. Lineage tracing is a valuable method for tracing specific cells and achieving a better understanding of the CNS. Recently, various technological breakthroughs have been made in lineage tracing, such as the application of various combinations of fluorescent reporters and advances in barcode technology. The development of lineage tracing has given us a deeper understanding of the normal physiology of the CNS, especially the pathological processes. In this review, we summarize these advances of lineage tracing and their applications in CNS. We focus on the use of lineage tracing techniques to elucidate the process CNS development and especially the mechanism of injury repair. Deep understanding of the central nervous system will help us to use existing technologies to diagnose and treat diseases.
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Affiliation(s)
- Hao Li
- Department of Neurosurgery, Beijing Tian tan Hospital, Capital Medical University, Beijing, China
| | - Yuan Zhuang
- Department of Neurosurgery, Beijing Tian tan Hospital, Capital Medical University, Beijing, China
| | - Bin Zhang
- Department of Intensive Care Unit, Beijing Tian tan Hospital, Capital Medical University, Beijing, China
| | - Xiaojian Xu
- Beijing Key Laboratory of Central Nervous System Injury, Department of Neurotrauma, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Baiyun Liu
- Department of Neurosurgery, Beijing Tian tan Hospital, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Central Nervous System Injury, Department of Neurotrauma, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
- Center for Nerve Injury and Repair, Beijing Institute of Brain Disorders, Beijing, China.
- China National Clinical Research Center for Neurological Diseases, Beijing, China.
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6
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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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7
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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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8
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Pan X, Zhang X. Studying temporal dynamics of single cells: expression, lineage and regulatory networks. Biophys Rev 2024; 16:57-67. [PMID: 38495440 PMCID: PMC10937865 DOI: 10.1007/s12551-023-01090-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
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Affiliation(s)
- Xinhai Pan
- 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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9
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Xue Y, Su Z, Lin X, Ho MK, Yu KHO. Single-cell lineage tracing with endogenous markers. Biophys Rev 2024; 16:125-139. [PMID: 38495438 PMCID: PMC10937880 DOI: 10.1007/s12551-024-01179-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/18/2024] [Indexed: 03/19/2024] Open
Abstract
Resolving lineage relationships between cells in an organism provides key insights into the fate of individual cells and drives a fundamental understanding of the process of development and disease. A recent rapid increase in experimental and computational advances for detecting naturally occurring somatic nuclear and mitochondrial mutation at single-cell resolution has expanded lineage tracing from model organisms to humans. This review discusses the advantages and challenges of experimental and computational techniques for cell lineage tracing using somatic mutation as endogenous DNA barcodes to decipher the relationships between cells during development and tumour evolution. We outlook the advantages of spatial clonal evolution analysis and single-cell lineage tracing using endogenous genetic markers.
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Affiliation(s)
- Yan Xue
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Zezhuo Su
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xinyi Lin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Mun Kay Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ken H. O. Yu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
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10
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Yu P, Cheng L. Lineage Tracing by Single-Cell Transcriptomics Decoding Dynamics of Lineage Commitment. Methods Mol Biol 2024; 2736:1-7. [PMID: 36749487 DOI: 10.1007/7651_2022_476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Tracing the fate of individual cells and their progeny is necessary and significant for stem cell research and cancer research. Changes in lineage-specific transcription factor levels during lineage commitment are gradual and continuous. Development of single-cell sequencing technology allows many different states of cells to be sequenced at an unprecedented resolution, and it has been proved that single-cell transcriptomics meets lineage tracing. Here, we introduce a detailed protocol for the lineage tracing by single-cell transcriptomics to clarify the dynamics of lineage commitment.
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Affiliation(s)
- Ping Yu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lin Cheng
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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11
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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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12
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [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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13
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Deng S, He X. Tree of life at two levels: from species to cell. Sci Bull (Beijing) 2023; 68:2515-2518. [PMID: 37778944 DOI: 10.1016/j.scib.2023.09.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Affiliation(s)
- Shanjun Deng
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Xionglei He
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China.
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14
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Xie J, Jeon H, Xin G, Ma Q, Chung D. LRT: Integrative analysis of scRNA-seq and scTCR-seq data to investigate clonal differentiation heterogeneity. PLoS Comput Biol 2023; 19:e1011300. [PMID: 37428794 PMCID: PMC10358952 DOI: 10.1371/journal.pcbi.1011300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 06/23/2023] [Indexed: 07/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) data has been widely used for cell trajectory inference, with the assumption that cells with similar expression profiles share the same differentiation state. However, the inferred trajectory may not reveal clonal differentiation heterogeneity among T cell clones. Single-cell T cell receptor sequencing (scTCR-seq) data provides invaluable insights into the clonal relationship among cells, yet it lacks functional characteristics. Therefore, scRNA-seq and scTCR-seq data complement each other in improving trajectory inference, where a reliable computational tool is still missing. We developed LRT, a computational framework for the integrative analysis of scTCR-seq and scRNA-seq data to explore clonal differentiation trajectory heterogeneity. Specifically, LRT uses the transcriptomics information from scRNA-seq data to construct overall cell trajectories and then utilizes both the TCR sequence information and phenotype information to identify clonotype clusters with distinct differentiation biasedness. LRT provides a comprehensive analysis workflow, including preprocessing, cell trajectory inference, clonotype clustering, trajectory biasedness evaluation, and clonotype cluster characterization. We illustrated its utility using scRNA-seq and scTCR-seq data of CD8+ T cells and CD4+ T cells with acute lymphocytic choriomeningitis virus infection. These analyses identified several clonotype clusters with distinct skewed distribution along the differentiation path, which cannot be revealed solely based on scRNA-seq data. Clones from different clonotype clusters exhibited diverse expansion capability, V-J gene usage pattern and CDR3 motifs. The LRT framework was implemented as an R package 'LRT', and it is now publicly accessible at https://github.com/JuanXie19/LRT. In addition, it provides two Shiny apps 'shinyClone' and 'shinyClust' that allow users to interactively explore distributions of clonotypes, conduct repertoire analysis, implement clustering of clonotypes, trajectory biasedness evaluation, and clonotype cluster characterization.
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Affiliation(s)
- Juan Xie
- The Interdisciplinary Program in Biostatistics, The Ohio State University, Columbus, Ohio, United States of America
| | - Hyeongseon Jeon
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Gang Xin
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, United States of America
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, Ohio, United States of America
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, United States of America
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15
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Pan X, Li H, Putta P, Zhang X. LinRace: single cell lineage reconstruction using paired lineage barcode and gene expression data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.12.536601. [PMID: 37090498 PMCID: PMC10120693 DOI: 10.1101/2023.04.12.536601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Understanding how single cells divide and differentiate into different cell types in developed organs is one of the major tasks of developmental and stem cell biology. Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for the reconstruction of the cell division tree, and even the detection of cell types and differentiation trajectories 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 expression data are emerging, aiming to improve the accuracy of lineage reconstruction. However, effectively incorporating the gene expression data requires a reasonable model on how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), a method that integrates the lineage barcode and gene expression data using the asymmetric cell division model and infers cell lineage under a framework combining Neighbor Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. Moreover, LinRace can output the cell states (cell types) of ancestral cells, which is rarely performed with existing lineage reconstruction methods. The information on ancestral cells can be used to analyze how a progenitor cell generates a large population of cells with various functionalities. LinRace is available at: https://github.com/ZhangLabGT/LinRace.
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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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16
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Sommer ER, Napoli GC, Chau CH, Price DK, Figg WD. Targeting the metastatic niche: Single-cell lineage tracing in prime time. iScience 2023; 26:106174. [PMID: 36895653 PMCID: PMC9988656 DOI: 10.1016/j.isci.2023.106174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Abstract
Identification of actionable drug targets remains a rate-limiting step of, and one of the most prominent barriers to successful drug development for metastatic cancers. CRISPR-Cas9, a tool for making targeted genomic edits, has given rise to various novel applications that have greatly accelerated discovery in developmental biology. Recent work has coupled a CRISPR-Cas9-based lineage tracing platform with single-cell transcriptomics in the unexplored context of cancer metastasis. In this perspective, we briefly reflect on the development of these distinct technological advances and the process by which they have become integrated. We also highlight the importance of single-cell lineage tracing in oncology drug development and suggest the profound capacity of a high-resolution, computational approach to reshape cancer drug discovery by enabling identification of novel metastasis-specific drug targets and mechanisms of resistance.
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Affiliation(s)
- Elijah R Sommer
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Giulia C Napoli
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Cindy H Chau
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Douglas K Price
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - William D Figg
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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17
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Chen C, Liao Y, Peng G. Connecting past and present: single-cell lineage tracing. Protein Cell 2022; 13:790-807. [PMID: 35441356 PMCID: PMC9237189 DOI: 10.1007/s13238-022-00913-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/06/2022] [Indexed: 01/16/2023] Open
Abstract
Central to the core principle of cell theory, depicting cells' history, state and fate is a fundamental goal in modern biology. By leveraging clonal analysis and single-cell RNA-seq technologies, single-cell lineage tracing provides new opportunities to interrogate both cell states and lineage histories. During the past few years, many strategies to achieve lineage tracing at single-cell resolution have been developed, and three of them (integration barcodes, polylox barcodes, and CRISPR barcodes) are noteworthy as they are amenable in experimentally tractable systems. Although the above strategies have been demonstrated in animal development and stem cell research, much care and effort are still required to implement these methods. Here we review the development of single-cell lineage tracing, major characteristics of the cell barcoding strategies, applications, as well as technical considerations and limitations, providing a guide to choose or improve the single-cell barcoding lineage tracing.
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Affiliation(s)
- Cheng Chen
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Yuanxin Liao
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guangdun Peng
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- Center for Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
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18
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Sankaran VG, Weissman JS, Zon LI. Cellular barcoding to decipher clonal dynamics in disease. Science 2022; 378:eabm5874. [PMID: 36227997 PMCID: PMC10111813 DOI: 10.1126/science.abm5874] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Cellular barcodes are distinct DNA sequences that enable one to track specific cells across time or space. Recent advances in our ability to detect natural or synthetic cellular barcodes, paired with single-cell readouts of cell state, have markedly increased our knowledge of clonal dynamics and genealogies of the cells that compose a variety of tissues and organs. These advances hold promise to redefine our view of human disease. Here, we provide an overview of cellular barcoding approaches, discuss applications to gain new insights into disease mechanisms, and provide an outlook on future applications. We discuss unanticipated insights gained through barcoding in studies of cancer and blood cell production and describe how barcoding can be applied to a growing array of medical fields, particularly with the increasing recognition of clonal contributions in human diseases.
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Affiliation(s)
- Vijay G Sankaran
- Division of Hematology and Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Harvard Stem Cell Institute, Cambridge, MA 02138, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.,David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.,Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Leonard I Zon
- Division of Hematology and Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Harvard Stem Cell Institute, Cambridge, MA 02138, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.,Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Harvard Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
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19
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van Vliet EA, Hildebrand MS, Mills JD, Brennan GP, Eid T, Masino SA, Whittemore V, Bindila L, Wang KK, Patel M, Perucca P, Reid CA. A companion to the preclinical common data elements for genomics, transcriptomics, and epigenomics data in rodent epilepsy models. A report of the TASK3-WG4 omics working group of the ILAE/AES joint translational TASK force. Epilepsia Open 2022. [PMID: 35950645 DOI: 10.1002/epi4.12640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 02/22/2022] [Indexed: 11/06/2022] Open
Abstract
The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force established the TASK3 working groups to create common data elements (CDEs) for various preclinical epilepsy research disciplines. The aim of the CDEs is to improve the standardization of experimental designs across a range of epilepsy research-related methods. Here, we have generated CDE tables with key parameters and case report forms (CRFs) containing the essential contents of the study protocols for genomics, transcriptomics, and epigenomics in rodent models of epilepsy, with a specific focus on adult rats and mice. We discuss the important elements that need to be considered for genomics, transcriptomics, and epigenomics methodologies, providing a rationale for the parameters that should be collected. This is the first in a two-part series of omics papers with the second installment to cover proteomics, lipidomics, and metabolomics in adult rodents.
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Affiliation(s)
- Erwin A van Vliet
- Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC location University of Amsterdam, Department of (Neuro)Pathology, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Michael S Hildebrand
- Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, Victoria, Australia
- Murdoch Children's Research Institute, The Royal Children's Hospital, Parkville, Victoria, Australia
| | - James D Mills
- Amsterdam UMC location University of Amsterdam, Department of (Neuro)Pathology, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Gary P Brennan
- UCD School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Dublin, Ireland
- FutureNeuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Tore Eid
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Susan A Masino
- Neuroscience Program and Psychology Department, Life Sciences Center, Trinity College, Hartford, Connecticut, USA
| | - Vicky Whittemore
- Division of Neuroscience, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Laura Bindila
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center of the Johannes Gutenberg University of Mainz, Mainz, Germany
| | - Kevin K Wang
- Department of Emergency Medicine, Psychiatry and Neuroscience, University of Florida, Gainesville, Florida, USA
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, North Florida/South Georgia Veterans Health System, Gainesville, Florida, USA
| | - Manisha Patel
- Department of Pharmaceutical Sciences, University of Colorado, Aurora, Colorado, USA
| | - Piero Perucca
- Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, Victoria, Australia
- Bladin-Berkovic Comprehensive Epilepsy Program, Austin Health, Heidelberg, Victoria, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Christopher A Reid
- Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, Victoria, Australia
- Murdoch Children's Research Institute, The Royal Children's Hospital, Parkville, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
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20
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Pandey K, Zafar H. Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET. Nucleic Acids Res 2022; 50:e86. [PMID: 35639499 PMCID: PMC9410915 DOI: 10.1093/nar/gkac412] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/27/2022] [Accepted: 05/17/2022] [Indexed: 11/27/2022] Open
Abstract
Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing the cell-state manifold and cell-fate plasticity for complex topologies. Here, we present MARGARET (https://github.com/Zafar-Lab/Margaret) for inferring single-cell trajectory and fate mapping for diverse dynamic cellular processes. MARGARET reconstructs complex trajectory topologies using a deep unsupervised metric learning and a graph-partitioning approach based on a novel connectivity measure, automatically detects terminal cell states, and generalizes the quantification of fate plasticity for complex topologies. On a diverse benchmark consisting of synthetic and real datasets, MARGARET outperformed state-of-the-art methods in recovering global topology and cell pseudotime ordering. For human hematopoiesis, MARGARET accurately identified all major lineages and associated gene expression trends and helped identify transitional progenitors associated with key branching events. For embryoid body differentiation, MARGARET identified novel transitional populations that were validated by bulk sequencing and functionally characterized different precursor populations in the mesoderm lineage. For colon differentiation, MARGARET characterized the lineage for BEST4/OTOP2 cells and the heterogeneity in goblet cell lineage in the colon under normal and inflamed ulcerative colitis conditions. Finally, we demonstrated that MARGARET can scale to large scRNA-seq datasets consisting of ∼ millions of cells.
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Affiliation(s)
- Kushagra Pandey
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - Hamim Zafar
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India.,Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur 208016, India.,Mehta Family Centre for Engineering in Medicine, Indian Institute of Technology Kanpur, Kanpur 208016, India
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21
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Yao M, Ren T, Pan Y, Xue X, Li R, Zhang L, Li Y, Huang K. A New Generation of Lineage Tracing Dynamically Records Cell Fate Choices. Int J Mol Sci 2022; 23:ijms23095021. [PMID: 35563412 PMCID: PMC9105840 DOI: 10.3390/ijms23095021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 11/16/2022] Open
Abstract
Reconstructing the development of lineage relationships and cell fate mapping has been a fundamental problem in biology. Using advanced molecular biology and single-cell RNA sequencing, we have profiled transcriptomes at the single-cell level and mapped cell fates during development. Recently, CRISPR/Cas9 barcode editing for large-scale lineage tracing has been used to reconstruct the pseudotime trajectory of cells and improve lineage tracing accuracy. This review presents the progress of the latest CbLT (CRISPR-based Lineage Tracing) and discusses the current limitations and potential technical pitfalls in their application and other emerging concepts.
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22
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Pan X, Li H, Zhang X. TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history. Nucleic Acids Res 2022; 50:4272-4288. [PMID: 35412632 PMCID: PMC9071466 DOI: 10.1093/nar/gkac235] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 03/23/2022] [Accepted: 03/31/2022] [Indexed: 11/18/2022] Open
Abstract
Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree and makes it possible to reconstruct ancestral cell types and trace the origin of each cell type. Meanwhile, trajectory inference methods are widely used to infer cell trajectories and pseudotime in a dynamic process using gene expression data of present-day cells. Here, we present TedSim (single-cell temporal dynamics simulator), which simulates the cell division events from the root cell to present-day cells, simultaneously generating two data modalities for each single cell: the lineage barcode and gene expression data. TedSim is a framework that connects the two problems: lineage tracing and trajectory inference. Using TedSim, we conducted analysis to show that (i) TedSim generates realistic gene expression and barcode data, as well as realistic relationships between these two data modalities; (ii) trajectory inference methods can recover the underlying cell state transition mechanism with balanced cell type compositions; and (iii) integrating gene expression and barcode data can provide more insights into the temporal dynamics in cell differentiation compared to using only one type of data, but better integration methods need to be developed.
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Affiliation(s)
- Xinhai Pan
- School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Hechen Li
- School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
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23
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Jovic D, Liang X, Zeng H, Lin L, Xu F, Luo Y. Single-cell RNA sequencing technologies and applications: A brief overview. Clin Transl Med 2022; 12:e694. [PMID: 35352511 PMCID: PMC8964935 DOI: 10.1002/ctm2.694] [Citation(s) in RCA: 281] [Impact Index Per Article: 140.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 12/09/2021] [Accepted: 12/20/2021] [Indexed: 12/19/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA-seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA-seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA-seq can provide unique information for better understanding health and diseases. One important application of the scRNA-seq technology is to build a better and high-resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment.
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Affiliation(s)
- Dragomirka Jovic
- Lars Bolund Institute of Regenerative MedicineQingdao‐Europe Advanced Institute for Life SciencesQingdaoChina
- BGI‐ShenzhenShenzhenChina
| | - Xue Liang
- Lars Bolund Institute of Regenerative MedicineQingdao‐Europe Advanced Institute for Life SciencesQingdaoChina
- BGI‐ShenzhenShenzhenChina
- Department of BiologyUniversity of CopenhagenCopenhagenDenmark
| | - Hua Zeng
- Nanjing University of Chinese MedicineNanjingChina
| | - Lin Lin
- Department of BiomedicineAarhus UniversityAarhusDenmark
- Steno Diabetes Center AarhusAarhus University HospitalAarhusDenmark
| | - Fengping Xu
- Lars Bolund Institute of Regenerative MedicineQingdao‐Europe Advanced Institute for Life SciencesQingdaoChina
- BGI‐ShenzhenShenzhenChina
| | - Yonglun Luo
- Lars Bolund Institute of Regenerative MedicineQingdao‐Europe Advanced Institute for Life SciencesQingdaoChina
- BGI‐ShenzhenShenzhenChina
- Department of BiomedicineAarhus UniversityAarhusDenmark
- Steno Diabetes Center AarhusAarhus University HospitalAarhusDenmark
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24
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Wang Y, Xie S, Armendariz D, Hon GC. Computational identification of clonal cells in single-cell CRISPR screens. BMC Genomics 2022; 23:135. [PMID: 35168568 PMCID: PMC8845350 DOI: 10.1186/s12864-022-08359-1] [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: 10/26/2021] [Accepted: 02/01/2022] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Single-cell CRISPR screens are powerful tools to understand genome function by linking genetic perturbations to transcriptome-wide phenotypes. However, since few cells can be affordably sequenced in these screens, biased sampling of cells could affect data interpretation. One potential source of biased sampling is clonal cell expansion. RESULTS Here, we identify clonal cells in single cell screens using multiplexed sgRNAs as barcodes. We find that the cells in each clone share transcriptional similarities and bear segmental copy number changes. These analyses suggest that clones are genetically distinct. Finally, we show that the transcriptional similarities of clonally expanded cells contribute to false positives in single-cell CRISPR screens. CONCLUSIONS Experimental conditions that reduce clonal expansion or computational filtering of clonal cells will improve the reliability of single-cell CRISPR screens.
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Affiliation(s)
- Yihan Wang
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Shiqi Xie
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Daniel Armendariz
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Gary C Hon
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
- Department of Bioinformatics, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
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25
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Ding J, Sharon N, Bar-Joseph Z. Temporal modelling using single-cell transcriptomics. Nat Rev Genet 2022; 23:355-368. [PMID: 35102309 DOI: 10.1038/s41576-021-00444-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2021] [Indexed: 12/16/2022]
Abstract
Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.
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26
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Ding J, Alavi A, Ebrahimkhani MR, Bar-Joseph Z. Computational tools for analyzing single-cell data in pluripotent cell differentiation studies. CELL REPORTS METHODS 2021; 1:100087. [PMID: 35474899 PMCID: PMC9017169 DOI: 10.1016/j.crmeth.2021.100087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Single-cell technologies are revolutionizing the ability of researchers to infer the causes and results of biological processes. Although several studies of pluripotent cell differentiation have recently utilized single-cell sequencing data, other aspects related to the optimization of differentiation protocols, their validation, robustness, and usage are still not taking full advantage of single-cell technologies. In this review, we focus on computational approaches for the analysis of single-cell omics and imaging data and discuss their use to address many of the major challenges involved in the development, validation, and use of cells obtained from pluripotent cell differentiation.
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Affiliation(s)
- Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, 1001 Decarie Boulevard, Montreal QC H4A 3J1, Canada
| | - Amir Alavi
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Mo R. Ebrahimkhani
- Department of Pathology, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
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27
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Forrow A, Schiebinger G. LineageOT is a unified framework for lineage tracing and trajectory inference. Nat Commun 2021; 12:4940. [PMID: 34400634 PMCID: PMC8367995 DOI: 10.1038/s41467-021-25133-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 07/08/2021] [Indexed: 12/20/2022] Open
Abstract
Understanding the genetic and epigenetic programs that control differentiation during development is a fundamental challenge, with broad impacts across biology and medicine. Measurement technologies like single-cell RNA-sequencing and CRISPR-based lineage tracing have opened new windows on these processes, through computational trajectory inference and lineage reconstruction. While these two mathematical problems are deeply related, methods for trajectory inference are not typically designed to leverage information from lineage tracing and vice versa. Here, we present LineageOT, a unified framework for lineage tracing and trajectory inference. Specifically, we leverage mathematical tools from graphical models and optimal transport to reconstruct developmental trajectories from time courses with snapshots of both cell states and lineages. We find that lineage data helps disentangle complex state transitions with increased accuracy using fewer measured time points. Moreover, integrating lineage tracing with trajectory inference in this way could enable accurate reconstruction of developmental pathways that are impossible to recover with state-based methods alone.
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Affiliation(s)
- Aden Forrow
- Mathematical Institute, University of Oxford, Oxford, UK.
| | - Geoffrey Schiebinger
- Department of Mathematics, University of British Columbia, Vancouver, BC, Canada.
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28
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Bode D, Cull AH, Rubio-Lara JA, Kent DG. Exploiting Single-Cell Tools in Gene and Cell Therapy. Front Immunol 2021; 12:702636. [PMID: 34322133 PMCID: PMC8312222 DOI: 10.3389/fimmu.2021.702636] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/28/2021] [Indexed: 12/12/2022] Open
Abstract
Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.
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Affiliation(s)
- Daniel Bode
- Wellcome Medical Research Council (MRC) Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Alyssa H. Cull
- York Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom
| | - Juan A. Rubio-Lara
- York Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom
| | - David G. Kent
- York Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom
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29
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Hayakawa Y, Nakagawa H, Rustgi AK, Que J, Wang TC. Stem cells and origins of cancer in the upper gastrointestinal tract. Cell Stem Cell 2021; 28:1343-1361. [PMID: 34129814 DOI: 10.1016/j.stem.2021.05.012] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The esophagus and stomach, joined by a unique transitional zone, contain actively dividing epithelial stem cells required for organ homeostasis. Upon prolonged inflammation, epithelial cells in both organs can undergo a cell fate switch leading to intestinal metaplasia, predisposing to malignancy. Here we discuss the biology of gastroesophageal stem cells and their role as cells of origin in cancer. We summarize the interactions between the stromal niche and gastroesophageal stem cells in metaplasia and early expansion of mutated stem-cell-derived clones during carcinogenesis. Finally, we review new approaches under development to better study gastroesophageal stem cells and advance the field.
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Affiliation(s)
- Yoku Hayakawa
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyoku, Tokyo 113-8655, Japan
| | - Hiroshi Nakagawa
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University, College of Physicians and Surgeons, 1130 St. Nicholas Avenue, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, 1130 St. Nicholas Avenue, New York, NY 10032, USA
| | - Anil K Rustgi
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University, College of Physicians and Surgeons, 1130 St. Nicholas Avenue, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, 1130 St. Nicholas Avenue, New York, NY 10032, USA
| | - Jianwen Que
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University, College of Physicians and Surgeons, 1130 St. Nicholas Avenue, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, 1130 St. Nicholas Avenue, New York, NY 10032, USA; Columbia Center for Human Development, Department of Medicine, Columbia University, College of Physicians and Surgeons, 1130 St. Nicholas Avenue, New York, NY 10032, USA.
| | - Timothy C Wang
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University, College of Physicians and Surgeons, 1130 St. Nicholas Avenue, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, 1130 St. Nicholas Avenue, New York, NY 10032, USA.
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30
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Wang L, Zhang Q, Qin Q, Trasanidis N, Vinyard M, Chen H, Pinello L. Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 26:1-11. [PMID: 33997529 PMCID: PMC8117397 DOI: 10.1016/j.coisb.2021.03.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Rapid technological advances in transcriptomics and lineage tracing technologies provide new opportunities to understand organismal development at the single-cell level. Building on these advances, various computational methods have been proposed to infer developmental trajectories and to predict cell fate. These methods have unveiled previously uncharacterized transitional cell types and differentiation processes. Importantly, the ability to recover cell states and trajectories has been evolving hand-in-hand with new technologies and diverse experimental designs; more recent methods can capture complex trajectory topologies and infer short- and long-term cell fate dynamics. Here, we summarize and categorize the most recent and popular computational approaches for trajectory inference based on the information they leverage and describe future challenges and opportunities for the development of new methods for reconstructing differentiation trajectories and inferring cell fates.
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Affiliation(s)
- Lingfei Wang
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Qian Zhang
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Qian Qin
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Nikolaos Trasanidis
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, UK
| | - Michael Vinyard
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Huidong Chen
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Luca Pinello
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
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31
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Quinn JJ, Jones MG, Okimoto RA, Nanjo S, Chan MM, Yosef N, Bivona TG, Weissman JS. Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science 2021; 371:eabc1944. [PMID: 33479121 PMCID: PMC7983364 DOI: 10.1126/science.abc1944] [Citation(s) in RCA: 138] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 09/23/2020] [Accepted: 12/17/2020] [Indexed: 12/11/2022]
Abstract
Detailed phylogenies of tumor populations can recount the history and chronology of critical events during cancer progression, such as metastatic dissemination. We applied a Cas9-based, single-cell lineage tracer to study the rates, routes, and drivers of metastasis in a lung cancer xenograft mouse model. We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination. This revealed stark heterogeneity in metastatic capacity, arising from preexisting and heritable differences in gene expression. We demonstrate that these identified genes can drive invasiveness and uncovered an unanticipated suppressive role for KRT17 We also show that metastases disseminated via multidirectional tissue routes and complex seeding topologies. Overall, we demonstrate the power of tracing cancer progression at subclonal resolution and vast scale.
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Affiliation(s)
- Jeffrey J Quinn
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Inscripta, Inc., Boulder, CO, USA
| | - Matthew G Jones
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Ross A Okimoto
- UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Shigeki Nanjo
- UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Michelle M Chan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA
- Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA, USA
| | - Trever G Bivona
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan S Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Whitehead Institute, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
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Iqbal F, Lupieri A, Aikawa M, Aikawa E. Harnessing Single-Cell RNA Sequencing to Better Understand How Diseased Cells Behave the Way They Do in Cardiovascular Disease. Arterioscler Thromb Vasc Biol 2021; 41:585-600. [PMID: 33327741 PMCID: PMC8105278 DOI: 10.1161/atvbaha.120.314776] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/03/2020] [Indexed: 02/06/2023]
Abstract
The transition of healthy arteries and cardiac valves into dense, cell-rich, calcified, and fibrotic tissues is driven by a complex interplay of both cellular and molecular mechanisms. Specific cell types in these cardiovascular tissues become activated following the exposure to systemic stimuli including circulating lipoproteins or inflammatory mediators. This activation induces multiple cascades of events where changes in cell phenotypes and activation of certain receptors may trigger multiple pathways and specific alterations to the transcriptome. Modifications to the transcriptome and proteome can give rise to pathological cell phenotypes and trigger mechanisms that exacerbate inflammation, proliferation, calcification, and recruitment of resident or distant cells. Accumulating evidence suggests that each cell type involved in vascular and valvular diseases is heterogeneous. Single-cell RNA sequencing is a transforming medical research tool that enables the profiling of the unique fingerprints at single-cell levels. Its applications have allowed the construction of cell atlases including the mammalian heart and tissue vasculature and the discovery of new cell types implicated in cardiovascular disease. Recent advances in single-cell RNA sequencing have facilitated the identification of novel resident cell populations that become activated during disease and has allowed tracing the transition of healthy cells into pathological phenotypes. Furthermore, single-cell RNA sequencing has permitted the characterization of heterogeneous cell subpopulations with unique genetic profiles in healthy and pathological cardiovascular tissues. In this review, we highlight the latest groundbreaking research that has improved our understanding of the pathological mechanisms of atherosclerosis and future directions for calcific aortic valve disease.
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Affiliation(s)
- Farwah Iqbal
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Adrien Lupieri
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Masanori Aikawa
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Elena Aikawa
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Human Pathology, Sechenov First Moscow State Medical University, Moscow, 119992, Russia
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Massoz L, Dupont MA, Manfroid I. Zebra-Fishing for Regenerative Awakening in Mammals. Biomedicines 2021; 9:65. [PMID: 33445518 PMCID: PMC7827770 DOI: 10.3390/biomedicines9010065] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 02/07/2023] Open
Abstract
Regeneration is defined as the ability to regrow an organ or a tissue destroyed by degeneration or injury. Many human degenerative diseases and pathologies, currently incurable, could be cured if functional tissues or cells could be restored. Unfortunately, humans and more generally mammals have limited regenerative capabilities, capacities that are even further declining with age, contrary to simpler organisms. Initially thought to be lost during evolution, several studies have revealed that regenerative mechanisms are still present in mammals but are latent and thus they could be stimulated. To do so there is a pressing need to identify the fundamental mechanisms of regeneration in species able to efficiently regenerate. Thanks to its ability to regenerate most of its organs and tissues, the zebrafish has become a powerful model organism in regenerative biology and has recently engendered a number of studies attesting the validity of awakening the regenerative potential in mammals. In this review we highlight studies, particularly in the liver, pancreas, retina, heart, brain and spinal cord, which have identified conserved regenerative molecular events that proved to be beneficial to restore murine and even human cells and which helped clarify the real clinical translation potential of zebrafish research to mammals.
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Affiliation(s)
| | | | - Isabelle Manfroid
- Zebrafish Development and Disease Models Laboratory, GIGA-Stem Cells, University of Liège, B-4000 Liège, Belgium; (L.M.); (M.A.D.)
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Mehtonen J, Teppo S, Lahnalampi M, Kokko A, Kaukonen R, Oksa L, Bouvy-Liivrand M, Malyukova A, Mäkinen A, Laukkanen S, Mäkinen PI, Rounioja S, Ruusuvuori P, Sangfelt O, Lund R, Lönnberg T, Lohi O, Heinäniemi M. Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities. Genome Med 2020; 12:99. [PMID: 33218352 PMCID: PMC7679990 DOI: 10.1186/s13073-020-00799-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 11/03/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Tight regulatory loops orchestrate commitment to B cell fate within bone marrow. Genetic lesions in this gene regulatory network underlie the emergence of the most common childhood cancer, acute lymphoblastic leukemia (ALL). The initial genetic hits, including the common translocation that fuses ETV6 and RUNX1 genes, lead to arrested cell differentiation. Here, we aimed to characterize transcription factor activities along the B-lineage differentiation trajectory as a reference to characterize the aberrant cell states present in leukemic bone marrow, and to identify those transcription factors that maintain cancer-specific cell states for more precise therapeutic intervention. METHODS We compared normal B-lineage differentiation and in vivo leukemic cell states using single cell RNA-sequencing (scRNA-seq) and several complementary genomics profiles. Based on statistical tools for scRNA-seq, we benchmarked a workflow to resolve transcription factor activities and gene expression distribution changes in healthy bone marrow lymphoid cell states. We compared these to ALL bone marrow at diagnosis and in vivo during chemotherapy, focusing on leukemias carrying the ETV6-RUNX1 fusion. RESULTS We show that lymphoid cell transcription factor activities uncovered from bone marrow scRNA-seq have high correspondence with independent ATAC- and ChIP-seq data. Using this comprehensive reference for regulatory factors coordinating B-lineage differentiation, our analysis of ETV6-RUNX1-positive ALL cases revealed elevated activity of multiple ETS-transcription factors in leukemic cells states, including the leukemia genome-wide association study hit ELK3. The accompanying gene expression changes associated with natural killer cell inactivation and depletion in the leukemic immune microenvironment. Moreover, our results suggest that the abundance of G1 cell cycle state at diagnosis and lack of differentiation-associated regulatory network changes during induction chemotherapy represent features of chemoresistance. To target the leukemic regulatory program and thereby overcome treatment resistance, we show that inhibition of ETS-transcription factors reduced cell viability and resolved pathways contributing to this using scRNA-seq. CONCLUSIONS Our data provide a detailed picture of the transcription factor activities characterizing both normal B-lineage differentiation and those acquired in leukemic bone marrow and provide a rational basis for new treatment strategies targeting the immune microenvironment and the active regulatory network in leukemia.
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Affiliation(s)
- Juha Mehtonen
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Yliopistonranta 1, FI-70211, Kuopio, Finland
| | - Susanna Teppo
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, FI-33014, Tampere, Finland
| | - Mari Lahnalampi
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Yliopistonranta 1, FI-70211, Kuopio, Finland
| | - Aleksi Kokko
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Yliopistonranta 1, FI-70211, Kuopio, Finland
| | - Riina Kaukonen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Laura Oksa
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, FI-33014, Tampere, Finland
| | - Maria Bouvy-Liivrand
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Yliopistonranta 1, FI-70211, Kuopio, Finland
| | - Alena Malyukova
- Department of Cell and Molecular Biology, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Artturi Mäkinen
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, FI-33014, Tampere, Finland
| | - Saara Laukkanen
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, FI-33014, Tampere, Finland
| | - Petri I Mäkinen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Yliopistonranta 1, FI-70211, Kuopio, Finland
| | | | - Pekka Ruusuvuori
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, FI-33014, Tampere, Finland
| | - Olle Sangfelt
- Department of Cell and Molecular Biology, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Riikka Lund
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Tapio Lönnberg
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Olli Lohi
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, FI-33014, Tampere, Finland
- Tays Cancer Centre, Tampere University Hospital, Tampere, Finland
| | - Merja Heinäniemi
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Yliopistonranta 1, FI-70211, Kuopio, Finland.
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Yeldell SB, Yang L, Lee J, Eberwine JH, Dmochowski IJ. Oligonucleotide Probe for Transcriptome in Vivo Analysis (TIVA) of Single Neurons with Minimal Background. ACS Chem Biol 2020; 15:2714-2721. [PMID: 32902259 DOI: 10.1021/acschembio.0c00499] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Messenger RNA (mRNA) isolated from single cells can generate powerful biological insights, including the discovery of new cell types with unique functions as well as markers potentially predicting a cell's response to various therapeutic agents. We previously introduced an oligonucleotide-based technique for site-selective, photoinduced biotinylation and capture of mRNA within a living cell called transcriptome in vivo analysis (TIVA). Successful application of the TIVA technique hinges upon its oligonucleotide probe remaining completely inert (or "caged") to mRNA unless photoactivated. To improve the reliability of TIVA probe caging in diverse and challenging biological conditions, we applied a rational design process involving iterative modifications to the oligonucleotide construct. In this work, we discuss these design motivations and present an optimized probe with minimal background binding to mRNA prior to photolysis. We assess its caging performance through multiple in vitro assays including FRET analysis, native gel comigration, and pull down with model mRNA transcripts. Finally, we demonstrate that this improved probe can also isolate mRNA from single living neurons in brain tissue slices with excellent caging control.
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Affiliation(s)
- Sean B. Yeldell
- Department of Chemistry, University of Pennsylvania, 231 South 34 Street, Philadelphia, Pennsylvania 19104-6323, United States
| | - Linlin Yang
- Department of Chemistry, University of Pennsylvania, 231 South 34 Street, Philadelphia, Pennsylvania 19104-6323, United States
| | - Jaehee Lee
- Department of Pharmacology, University of Pennsylvania, 38 John Morgan Building, 3620 Hamilton Walk, Philadelphia, Pennsylvania 19104-6084, United States
| | - James H. Eberwine
- Department of Pharmacology, University of Pennsylvania, 38 John Morgan Building, 3620 Hamilton Walk, Philadelphia, Pennsylvania 19104-6084, United States
| | - Ivan J. Dmochowski
- Department of Chemistry, University of Pennsylvania, 231 South 34 Street, Philadelphia, Pennsylvania 19104-6323, United States
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