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Kimura H, Lahouel K, Tomasetti C, Roberts NJ. Functional characterization of all CDKN2A missense variants and comparison to in silico models of pathogenicity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573507. [PMID: 38234851 PMCID: PMC10793438 DOI: 10.1101/2023.12.28.573507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Interpretation of variants identified during genetic testing is a significant clinical challenge. In this study, we developed a high-throughput CDKN2A functional assay and characterized all possible CDKN2A missense variants. We found that 17.7% of all missense variants were functionally deleterious. We also used our functional classifications to assess the performance of in silico models that predict the effect of variants, including recently reported models based on machine learning. Notably, we found that all in silico models performed similarly when compared to our functional classifications with accuracies of 39.5-85.4%. Furthermore, while we found that functionally deleterious variants were enriched within ankyrin repeats, we did not identify any residues where all missense variants were functionally deleterious. Our functional classifications are a resource to aid the interpretation of CDKN2A variants and have important implications for the application of variant interpretation guidelines, particularly the use of in silico models for clinical variant interpretation.
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Affiliation(s)
- Hirokazu Kimura
- Department of Pathology, the Johns Hopkins University School of Medicine; Baltimore, 21287, USA
| | - Kamel Lahouel
- Division of Integrated Genomics, Translational Genomics Research Institute; Phoenix, 85004, USA
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope; Duarte, 91010, USA
| | - Cristian Tomasetti
- Division of Integrated Genomics, Translational Genomics Research Institute; Phoenix, 85004, USA
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope; Duarte, 91010, USA
| | - Nicholas J. Roberts
- Department of Pathology, the Johns Hopkins University School of Medicine; Baltimore, 21287, USA
- Department of Oncology, the Johns Hopkins University School of Medicine; Baltimore, 21287, USA
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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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Nadalin F, Marzi MJ, Pirra Piscazzi M, Fuentes-Bravo P, Procaccia S, Climent M, Bonetti P, Rubolino C, Giuliani B, Papatheodorou I, Marioni JC, Nicassio F. Multi-omic lineage tracing predicts the transcriptional, epigenetic and genetic determinants of cancer evolution. Nat Commun 2024; 15:7609. [PMID: 39218912 PMCID: PMC11366763 DOI: 10.1038/s41467-024-51424-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 08/05/2024] [Indexed: 09/04/2024] Open
Abstract
Cancer is a highly heterogeneous disease, where phenotypically distinct subpopulations coexist and can be primed to different fates. Both genetic and epigenetic factors may drive cancer evolution, however little is known about whether and how such a process is pre-encoded in cancer clones. Using single-cell multi-omic lineage tracing and phenotypic assays, we investigate the predictive features of either tumour initiation or drug tolerance within the same cancer population. Clones primed to tumour initiation in vivo display two distinct transcriptional states at baseline. Remarkably, these states share a distinctive DNA accessibility profile, highlighting an epigenetic basis for tumour initiation. The drug tolerant niche is also largely pre-encoded, but only partially overlaps the tumour-initiating one and evolves following two genetically and transcriptionally distinct trajectories. Our study highlights coexisting genetic, epigenetic and transcriptional determinants of cancer evolution, unravelling the molecular complexity of pre-encoded tumour phenotypes.
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Affiliation(s)
- F Nadalin
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
| | - M J Marzi
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy
| | - M Pirra Piscazzi
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy
| | - P Fuentes-Bravo
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy
| | - S Procaccia
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy
| | - M Climent
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy
| | - P Bonetti
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy
| | - C Rubolino
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy
| | - B Giuliani
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy
| | - I Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - J C Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - F Nicassio
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy.
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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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Fazzari E, Azizad DJ, Yu K, Ge W, Li MX, Nano PR, Kan RL, Tum HA, Tse C, Bayley NA, Haka V, Cadet D, Perryman T, Soto JA, Wick B, Raleigh DR, Crouch EE, Patel KS, Liau LM, Deneen B, Nathanson DA, Bhaduri A. Glioblastoma Neurovascular Progenitor Orchestrates Tumor Cell Type Diversity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.24.604840. [PMID: 39091877 PMCID: PMC11291138 DOI: 10.1101/2024.07.24.604840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Glioblastoma (GBM) is the deadliest form of primary brain tumor with limited treatment options. Recent studies have profiled GBM tumor heterogeneity, revealing numerous axes of variation that explain the molecular and spatial features of the tumor. Here, we seek to bridge descriptive characterization of GBM cell type heterogeneity with the functional role of individual populations within the tumor. Our lens leverages a gene program-centric meta-atlas of published transcriptomic studies to identify commonalities between diverse tumors and cell types in order to decipher the mechanisms that drive them. This approach led to the discovery of a tumor-derived stem cell population with mixed vascular and neural stem cell features, termed a neurovascular progenitor (NVP). Following in situ validation and molecular characterization of NVP cells in GBM patient samples, we characterized their function in vivo. Genetic depletion of NVP cells resulted in altered tumor cell composition, fewer cycling cells, and extended survival, underscoring their critical functional role. Clonal analysis of primary patient tumors in a human organoid tumor transplantation system demonstrated that the NVP has dual potency, generating both neuronal and vascular tumor cells. Although NVP cells comprise a small fraction of the tumor, these clonal analyses demonstrated that they strongly contribute to the total number of cycling cells in the tumor and generate a defined subset of the whole tumor. This study represents a paradigm by which cell type-specific interrogation of tumor populations can be used to study functional heterogeneity and therapeutically targetable vulnerabilities of GBM.
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Affiliation(s)
- Elisa Fazzari
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
| | - Daria J Azizad
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
| | - Kwanha Yu
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
| | - Weihong Ge
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
| | - Matthew X Li
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
| | - Patricia R Nano
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
| | - Ryan L Kan
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
| | - Hong A Tum
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Christopher Tse
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nicholas A Bayley
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Vjola Haka
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Dimitri Cadet
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Travis Perryman
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
| | - Jose A Soto
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
| | - Brittney Wick
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - David R Raleigh
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- Department of Pathology, University of California San Francisco, San Francisco, California, USA
| | - Elizabeth E Crouch
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA
| | - Kunal S Patel
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
| | - Benjamin Deneen
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Aparna Bhaduri
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, California, Los Angeles, CA, USA
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6
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Zhang Z, Melzer ME, Arun KM, Sun H, Eriksson CJ, Fabian I, Shaashua S, Kiani K, Oren Y, Goyal Y. Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms. CELL GENOMICS 2024; 4:100592. [PMID: 38925122 PMCID: PMC11293576 DOI: 10.1016/j.xgen.2024.100592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 03/26/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.
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Affiliation(s)
- Ziyang Zhang
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Madeline E Melzer
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Keerthana M Arun
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Hanxiao Sun
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Carl-Johan Eriksson
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Itai Fabian
- Department of Human Molecular Genetics & Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Sagi Shaashua
- Department of Human Molecular Genetics & Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Karun Kiani
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yaara Oren
- Department of Human Molecular Genetics & Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; CZ Biohub Chicago, Chicago, IL, USA.
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7
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Xu C, Alameri A, Leong W, Johnson E, Chen Z, Xu B, Leong KW. Multiscale engineering of brain organoids for disease modeling. Adv Drug Deliv Rev 2024; 210:115344. [PMID: 38810702 PMCID: PMC11265575 DOI: 10.1016/j.addr.2024.115344] [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: 02/13/2024] [Revised: 04/23/2024] [Accepted: 05/25/2024] [Indexed: 05/31/2024]
Abstract
Brain organoids hold great potential for modeling human brain development and pathogenesis. They recapitulate certain aspects of the transcriptional trajectory, cellular diversity, tissue architecture and functions of the developing brain. In this review, we explore the engineering strategies to control the molecular-, cellular- and tissue-level inputs to achieve high-fidelity brain organoids. We review the application of brain organoids in neural disorder modeling and emerging bioengineering methods to improve data collection and feature extraction at multiscale. The integration of multiscale engineering strategies and analytical methods has significant potential to advance insight into neurological disorders and accelerate drug development.
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Affiliation(s)
- Cong Xu
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Alia Alameri
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Wei Leong
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Emily Johnson
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Zaozao Chen
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Bin Xu
- Department of Psychiatry, Columbia University, New York, NY 10032, USA.
| | - Kam W Leong
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
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8
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Weiler P, Lange M, Klein M, Pe'er D, Theis F. CellRank 2: unified fate mapping in multiview single-cell data. Nat Methods 2024; 21:1196-1205. [PMID: 38871986 PMCID: PMC11239496 DOI: 10.1038/s41592-024-02303-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 05/09/2024] [Indexed: 06/15/2024]
Abstract
Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.
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Affiliation(s)
- Philipp Weiler
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Marius Lange
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Michal Klein
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Machine Learning Research, Apple, Paris, France
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Fabian Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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9
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Zaragoza MV, Bui TA, Widyastuti HP, Mehrabi M, Cang Z, Sha Y, Grosberg A, Nie Q. LMNA -Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-derived iPSC Differentiation Support Cell type and Lineage-specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598335. [PMID: 38915555 PMCID: PMC11195187 DOI: 10.1101/2024.06.12.598335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
LMNA -Related Dilated Cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C ( LMNA ) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. Molecular mechanisms of disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA -Related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (4 Patient and 8 Control) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for Cardiac Progenitors to Cardiomyocytes (CM) and Epicardium-Derived Cells (EPDC). Data integration and comparative analyses of Patient and Control cells found cell type and lineage differentially expressed genes (DEG) with enrichment to support pathway dysregulation. Top DEG and enriched pathways included: 10 ZNF genes and RNA polymerase II transcription in Pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CM; LMNA and epigenetic regulation and DDIT4 and mTORC1 signaling in EPDC. Top DEG also included: XIST and other X-linked genes, six imprinted genes: SNRPN , PWAR6 , NDN , PEG10 , MEG3 , MEG8 , and enriched gene sets in metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.
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10
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Vo DN, Yuan O, Kanaya M, Telliam-Dushime G, Li H, Kotova O, Caglar E, Honnens de Lichtenberg K, Rahman SH, Soneji S, Scheding S, Bryder D, Malmberg KJ, Sitnicka E. A temporal developmental map separates human NK cells from noncytotoxic ILCs through clonal and single-cell analysis. Blood Adv 2024; 8:2933-2951. [PMID: 38484189 PMCID: PMC11176970 DOI: 10.1182/bloodadvances.2023011909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 03/05/2024] [Indexed: 06/04/2024] Open
Abstract
ABSTRACT Natural killer (NK) cells represent the cytotoxic member within the innate lymphoid cell (ILC) family that are important against viral infections and cancer. Although the NK cell emergence from hematopoietic stem and progenitor cells through multiple intermediate stages and the underlying regulatory gene network has been extensively studied in mice, this process is not well characterized in humans. Here, using a temporal in vitro model to reconstruct the developmental trajectory of NK lineage, we identified an ILC-restricted oligopotent stage 3a CD34-CD117+CD161+CD45RA+CD56- progenitor population, that exclusively gave rise to CD56-expressing ILCs in vitro. We also further investigated a previously nonappreciated heterogeneity within the CD56+CD94-NKp44+ subset, phenotypically equivalent to stage 3b population containing both group-1 ILC and RORγt+ ILC3 cells, that could be further separated based on their differential expression of DNAM-1 and CD161 receptors. We confirmed that DNAM-1hi S3b and CD161hiCD117hi ILC3 populations distinctively differed in their expression of effector molecules, cytokine secretion, and cytotoxic activity. Furthermore, analysis of lineage output using DNA-barcode tracing across these stages supported a close developmental relationship between S3b-NK and S4-NK (CD56+CD94+) cells, whereas distant to the ILC3 subset. Cross-referencing gene signatures of culture-derived NK cells and other noncytotoxic ILCs with publicly available data sets validated that these in vitro stages highly resemble transcriptional profiles of respective in vivo ILC counterparts. Finally, by integrating RNA velocity and gene network analysis through single-cell regulatory network inference and clustering we unravel a network of coordinated and highly dynamic regulons driving the cytotoxic NK cell program, as a guide map for future studies on NK cell regulation.
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Affiliation(s)
- Dang Nghiem Vo
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Ouyang Yuan
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Minoru Kanaya
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Gladys Telliam-Dushime
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Hongzhe Li
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Olga Kotova
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Emel Caglar
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Cell Therapy Research, Novo Nordisk A/S, Måløv, Copenhagen, Denmark
| | | | | | - Shamit Soneji
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Stefan Scheding
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Department of Hematology, Skåne University Hospital, Lund, Sweden
| | - David Bryder
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Karl-Johan Malmberg
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
- Department of Medicine Huddinge, Center for Infectious Medicine, Karolinska Institute, Stockholm, Sweden
| | - Ewa Sitnicka
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
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11
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Finlay JB, Ireland AS, Hawgood SB, Reyes T, Ko T, Olsen RR, Abi Hachem R, Jang DW, Bell D, Chan JM, Goldstein BJ, Oliver TG. Olfactory neuroblastoma mimics molecular heterogeneity and lineage trajectories of small-cell lung cancer. Cancer Cell 2024; 42:1086-1105.e13. [PMID: 38788720 PMCID: PMC11186085 DOI: 10.1016/j.ccell.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/13/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024]
Abstract
The olfactory epithelium undergoes neuronal regeneration from basal stem cells and is susceptible to olfactory neuroblastoma (ONB), a rare tumor of unclear origins. Employing alterations in Rb1/Trp53/Myc (RPM), we establish a genetically engineered mouse model of high-grade metastatic ONB exhibiting a NEUROD1+ immature neuronal phenotype. We demonstrate that globose basal cells (GBCs) are a permissive cell of origin for ONB and that ONBs exhibit cell fate heterogeneity that mimics normal GBC developmental trajectories. ASCL1 loss in RPM ONB leads to emergence of non-neuronal histopathologies, including a POU2F3+ microvillar-like state. Similar to small-cell lung cancer (SCLC), mouse and human ONBs exhibit mutually exclusive NEUROD1 and POU2F3-like states, an immune-cold tumor microenvironment, intratumoral cell fate heterogeneity comprising neuronal and non-neuronal lineages, and cell fate plasticity-evidenced by barcode-based lineage tracing and single-cell transcriptomics. Collectively, our findings highlight conserved similarities between ONB and neuroendocrine tumors with significant implications for ONB classification and treatment.
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Affiliation(s)
- John B Finlay
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - Abbie S Ireland
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA
| | - Sarah B Hawgood
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA
| | - Tony Reyes
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA; Department of Oncological Sciences, University of Utah, Salt Lake City 84112, UT, USA
| | - Tiffany Ko
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - Rachelle R Olsen
- Department of Oncological Sciences, University of Utah, Salt Lake City 84112, UT, USA
| | - Ralph Abi Hachem
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - David W Jang
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - Diana Bell
- Division of Anatomic Pathology, City of Hope Comprehensive Cancer Center, Duarte 91010, CA, USA
| | - Joseph M Chan
- Human Oncology and Pathogenesis Program, Memorial-Sloan Kettering Cancer Center, New York City 10065, NY, USA
| | - Bradley J Goldstein
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA; Department of Neurobiology, Duke University, Durham 27710, NC, USA.
| | - Trudy G Oliver
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA; Department of Oncological Sciences, University of Utah, Salt Lake City 84112, UT, USA.
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12
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Lindhout FW, Krienen FM, Pollard KS, Lancaster MA. A molecular and cellular perspective on human brain evolution and tempo. Nature 2024; 630:596-608. [PMID: 38898293 DOI: 10.1038/s41586-024-07521-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 04/29/2024] [Indexed: 06/21/2024]
Abstract
The evolution of the modern human brain was accompanied by distinct molecular and cellular specializations, which underpin our diverse cognitive abilities but also increase our susceptibility to neurological diseases. These features, some specific to humans and others shared with related species, manifest during different stages of brain development. In this multi-stage process, neural stem cells proliferate to produce a large and diverse progenitor pool, giving rise to excitatory or inhibitory neurons that integrate into circuits during further maturation. This process unfolds over varying time scales across species and has progressively become slower in the human lineage, with differences in tempo correlating with differences in brain size, cell number and diversity, and connectivity. Here we introduce the terms 'bradychrony' and 'tachycrony' to describe slowed and accelerated developmental tempos, respectively. We review how recent technical advances across disciplines, including advanced engineering of in vitro models, functional comparative genetics and high-throughput single-cell profiling, are leading to a deeper understanding of how specializations of the human brain arise during bradychronic neurodevelopment. Emerging insights point to a central role for genetics, gene-regulatory networks, cellular innovations and developmental tempo, which together contribute to the establishment of human specializations during various stages of neurodevelopment and at different points in evolution.
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Affiliation(s)
- Feline W Lindhout
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge, UK.
| | - Fenna M Krienen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Katherine S Pollard
- Gladstone Institutes, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Department of Epidemiology & Biostatistics, Institute for Computational Health Sciences, and Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Madeline A Lancaster
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge, UK.
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13
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Aalam SMM, Nguyen LV, Ritting ML, Kannan N. Clonal tracking in cancer and metastasis. Cancer Metastasis Rev 2024; 43:639-656. [PMID: 37910295 DOI: 10.1007/s10555-023-10149-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 10/16/2023] [Indexed: 11/03/2023]
Abstract
The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.
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Affiliation(s)
| | - Long Viet Nguyen
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Megan L Ritting
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Nagarajan Kannan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
- Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA.
- Center for Regenerative Biotherapeutics, Mayo Clinic, Rochester, MN, USA.
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14
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Jindal K, Adil MT, Yamaguchi N, Yang X, Wang HC, Kamimoto K, Rivera-Gonzalez GC, Morris SA. Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes. Nat Biotechnol 2024; 42:946-959. [PMID: 37749269 PMCID: PMC11180607 DOI: 10.1038/s41587-023-01931-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 07/31/2023] [Indexed: 09/27/2023]
Abstract
Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage-tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics. However, reliance on transcriptional profiling limits adaptation to other single-cell assays. With CellTag-multi, we present an approach that enables direct capture of heritable random barcodes expressed as polyadenylated transcripts, in both single-cell RNA sequencing and single-cell Assay for Transposase Accessible Chromatin using sequencing assays, allowing for independent clonal tracking of transcriptional and epigenomic cell states. We validate CellTag-multi to characterize progenitor cell lineage priming during mouse hematopoiesis. Additionally, in direct reprogramming of fibroblasts to endoderm progenitors, we identify core regulatory programs underlying on-target and off-target fates. Furthermore, we reveal the transcription factor Zfp281 as a regulator of reprogramming outcome, biasing cells toward an off-target mesenchymal fate. Our results establish CellTag-multi as a lineage-tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.
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Affiliation(s)
- Kunal Jindal
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Mohd Tayyab Adil
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Naoto Yamaguchi
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Xue Yang
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Helen C Wang
- Department of Pediatrics, Division of Hematology and Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kenji Kamimoto
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Guillermo C Rivera-Gonzalez
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Samantha A Morris
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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15
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Cheng YL, Banu MA, Zhao W, Rosenfeld SS, Canoll P, Sims PA. Multiplexed single-cell lineage tracing of mitotic kinesin inhibitor resistance in glioblastoma. Cell Rep 2024; 43:114139. [PMID: 38652658 PMCID: PMC11199018 DOI: 10.1016/j.celrep.2024.114139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 03/01/2024] [Accepted: 04/08/2024] [Indexed: 04/25/2024] Open
Abstract
Glioblastoma (GBM) is a deadly brain tumor, and the kinesin motor KIF11 is an attractive therapeutic target with roles in proliferation and invasion. Resistance to KIF11 inhibitors, which has mainly been studied in animal models, presents significant challenges. We use lineage-tracing barcodes and single-cell RNA sequencing to analyze resistance in patient-derived GBM neurospheres treated with ispinesib, a potent KIF11 inhibitor. Similar to GBM progression in patients, untreated cells lose their neural lineage identity and become mesenchymal, which is associated with poor prognosis. Conversely, cells subjected to long-term ispinesib treatment exhibit a proneural phenotype. We generate patient-derived xenografts and show that ispinesib-resistant cells form less aggressive tumors in vivo, even in the absence of drug. Moreover, treatment of human ex vivo GBM slices with ispinesib demonstrates phenotypic alignment with in vitro responses, underscoring the clinical relevance of our findings. Finally, using retrospective lineage tracing, we identify drugs that are synergistic with ispinesib.
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Affiliation(s)
- Yim Ling Cheng
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Matei A Banu
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Wenting Zhao
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | - Peter Canoll
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Peter A Sims
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY 10032, USA.
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16
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Holze H, Talarmain L, Fennell KA, Lam EY, Dawson MA, Vassiliadis D. Analysis of synthetic cellular barcodes in the genome and transcriptome with BARtab and bartools. CELL REPORTS METHODS 2024; 4:100763. [PMID: 38670101 PMCID: PMC11133760 DOI: 10.1016/j.crmeth.2024.100763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/25/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024]
Abstract
Cellular barcoding is a lineage-tracing methodology that couples heritable synthetic barcodes to high-throughput sequencing, enabling the accurate tracing of cell lineages across a range of biological contexts. Recent studies have extended these methods by incorporating lineage information into single-cell or spatial transcriptomics readouts. Leveraging the rich biological information within these datasets requires dedicated computational tools for dataset pre-processing and analysis. Here, we present BARtab, a portable and scalable Nextflow pipeline, and bartools, an open-source R package, designed to provide an integrated end-to-end cellular barcoding analysis toolkit. BARtab and bartools contain methods to simplify the extraction, quality control, analysis, and visualization of lineage barcodes from population-level, single-cell, and spatial transcriptomics experiments. We showcase the utility of our integrated BARtab and bartools workflow via the analysis of exemplar bulk, single-cell, and spatial transcriptomics experiments containing cellular barcoding information.
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Affiliation(s)
- Henrietta Holze
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Laure Talarmain
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Katie A Fennell
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Enid Y Lam
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Mark A Dawson
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia; The University of Melbourne Centre for Cancer Research, The University of Melbourne, Melbourne, VIC 3000, Australia.
| | - Dane Vassiliadis
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia.
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17
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Youlten SE, Miao L, Hoppe C, Boswell CW, Musaev D, Abdelmessih M, Krishnaswamy S, Tornini VA, Giraldez AJ. Novel cell states arise in embryonic cells devoid of key reprogramming factors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.13.593729. [PMID: 38798464 PMCID: PMC11118305 DOI: 10.1101/2024.05.13.593729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The capacity for embryonic cells to differentiate relies on a large-scale reprogramming of the oocyte and sperm nucleus into a transient totipotent state. In zebrafish, this reprogramming step is achieved by the pioneer factors Nanog, Pou5f3, and Sox19b (NPS). Yet, it remains unclear whether cells lacking this reprogramming step are directed towards wild type states or towards novel developmental canals in the Waddington landscape of embryonic development. Here we investigate the developmental fate of embryonic cells mutant for NPS by analyzing their single-cell gene expression profiles. We find that cells lacking the first developmental reprogramming steps can acquire distinct cell states. These states are manifested by gene expression modules that result from a failure of nuclear reprogramming, the persistence of the maternal program, and the activation of somatic compensatory programs. As a result, most mutant cells follow new developmental canals and acquire new mixed cell states in development. In contrast, a group of mutant cells acquire primordial germ cell-like states, suggesting that NPS-dependent reprogramming is dispensable for these cell states. Together, these results demonstrate that developmental reprogramming after fertilization is required to differentiate most canonical developmental programs, and loss of the transient totipotent state canalizes embryonic cells into new developmental states in vivo.
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Affiliation(s)
- Scott E. Youlten
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Liyun Miao
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Caroline Hoppe
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Curtis W. Boswell
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Damir Musaev
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Mario Abdelmessih
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
- Current Address: AstraZeneca, Waltham, MA 02451, USA
| | - Smita Krishnaswamy
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
- Department of Computer Science, Yale University, New Haven, CT 06510, USA
| | - Valerie A. Tornini
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Antonio J. Giraldez
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
- Yale Stem Cell Center, Yale University School of Medicine, New Haven, CT 06510, USA
- Yale Cancer Center, Yale University School of Medicine, New Haven, CT 06510, USA
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18
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Lorenzo-Martín LF, Hübscher T, Bowler AD, Broguiere N, Langer J, Tillard L, Nikolaev M, Radtke F, Lutolf MP. Spatiotemporally resolved colorectal oncogenesis in mini-colons ex vivo. Nature 2024; 629:450-457. [PMID: 38658753 PMCID: PMC11078756 DOI: 10.1038/s41586-024-07330-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/18/2024] [Indexed: 04/26/2024]
Abstract
Three-dimensional organoid culture technologies have revolutionized cancer research by allowing for more realistic and scalable reproductions of both tumour and microenvironmental structures1-3. This has enabled better modelling of low-complexity cancer cell behaviours that occur over relatively short periods of time4. However, available organoid systems do not capture the intricate evolutionary process of cancer development in terms of tissue architecture, cell diversity, homeostasis and lifespan. As a consequence, oncogenesis and tumour formation studies are not possible in vitro and instead require the extensive use of animal models, which provide limited spatiotemporal resolution of cellular dynamics and come at a considerable cost in terms of resources and animal lives. Here we developed topobiologically complex mini-colons that are able to undergo tumorigenesis ex vivo by integrating microfabrication, optogenetic and tissue engineering approaches. With this system, tumorigenic transformation can be spatiotemporally controlled by directing oncogenic activation through blue-light exposure, and emergent colon tumours can be tracked in real-time at the single-cell resolution for several weeks without breaking the culture. These induced mini-colons display rich intratumoural and intertumoural diversity and recapitulate key pathophysiological hallmarks displayed by colorectal tumours in vivo. By fine-tuning cell-intrinsic and cell-extrinsic parameters, mini-colons can be used to identify tumorigenic determinants and pharmacological opportunities. As a whole, our study paves the way for cancer initiation research outside living organisms.
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Affiliation(s)
- L Francisco Lorenzo-Martín
- Laboratory of Stem Cell Bioengineering, Institute of Bioengineering, School of Life Sciences and School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Tania Hübscher
- Laboratory of Stem Cell Bioengineering, Institute of Bioengineering, School of Life Sciences and School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Amber D Bowler
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Nicolas Broguiere
- Laboratory of Stem Cell Bioengineering, Institute of Bioengineering, School of Life Sciences and School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jakob Langer
- Laboratory of Stem Cell Bioengineering, Institute of Bioengineering, School of Life Sciences and School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lucie Tillard
- Laboratory of Stem Cell Bioengineering, Institute of Bioengineering, School of Life Sciences and School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mikhail Nikolaev
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Freddy Radtke
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Matthias P Lutolf
- Laboratory of Stem Cell Bioengineering, Institute of Bioengineering, School of Life Sciences and School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
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19
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Wang K, Hou L, Wang X, Zhai X, Lu Z, Zi Z, Zhai W, He X, Curtis C, Zhou D, Hu Z. PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes. Nat Biotechnol 2024; 42:778-789. [PMID: 37524958 DOI: 10.1038/s41587-023-01887-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 06/28/2023] [Indexed: 08/02/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.
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Affiliation(s)
- Kun Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- School of Mathematical Sciences, Xiamen University, Xiamen, China
| | - Liangzhen Hou
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Xin Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiangwei Zhai
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Zhaolian Lu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhike Zi
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weiwei Zhai
- CAS Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Xionglei He
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Christina Curtis
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
| | - Zheng Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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20
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Umeyama T, Matsuda T, Nakashima K. Lineage Reprogramming: Genetic, Chemical, and Physical Cues for Cell Fate Conversion with a Focus on Neuronal Direct Reprogramming and Pluripotency Reprogramming. Cells 2024; 13:707. [PMID: 38667322 PMCID: PMC11049106 DOI: 10.3390/cells13080707] [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: 04/02/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
Although lineage reprogramming from one cell type to another is becoming a breakthrough technology for cell-based therapy, several limitations remain to be overcome, including the low conversion efficiency and subtype specificity. To address these, many studies have been conducted using genetics, chemistry, physics, and cell biology to control transcriptional networks, signaling cascades, and epigenetic modifications during reprogramming. Here, we summarize recent advances in cellular reprogramming and discuss future directions.
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Affiliation(s)
- Taichi Umeyama
- Department of Stem Cell Biology and Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka 819-0395, Japan
| | - Taito Matsuda
- Department of Stem Cell Biology and Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka 819-0395, Japan
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21
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Feng DC, Zhu WZ, Wang J, Li DX, Shi X, Xiong Q, You J, Han P, Qiu S, Wei Q, Yang L. The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy. Mil Med Res 2024; 11:21. [PMID: 38605399 PMCID: PMC11007901 DOI: 10.1186/s40779-024-00526-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 03/25/2024] [Indexed: 04/13/2024] Open
Abstract
In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.
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Affiliation(s)
- De-Chao Feng
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Division of Surgery & Interventional Science, University College London, London, WC1E 6BT, UK.
| | - Wei-Zhen Zhu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jie Wang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Deng-Xiong Li
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xu Shi
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiao Xiong
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jia You
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ping Han
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Shi Qiu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiang Wei
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Lu Yang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
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22
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Robey RW, Fitzsimmons CM, Guiblet WM, Frye WJ, Dalmasy JMG, Wang L, Russell DA, Huff LM, Perciaccante AJ, Ali-Rahmani F, Lipsey CC, Wade HM, Mitchell AV, Maligireddy SS, Terrero D, Butcher D, Edmondson EF, Jenkins LM, Nikitina T, Zhurkin VB, Tiwari AK, Piscopio AD, Totah RA, Bates SE, Arda HE, Gottesman MM, Batista PJ. The Methyltransferases METTL7A and METTL7B Confer Resistance to Thiol-Based Histone Deacetylase Inhibitors. Mol Cancer Ther 2024; 23:464-477. [PMID: 38151817 PMCID: PMC11223745 DOI: 10.1158/1535-7163.mct-23-0144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 08/25/2023] [Accepted: 12/20/2023] [Indexed: 12/29/2023]
Abstract
Histone deacetylase inhibitors (HDACi) are part of a growing class of epigenetic therapies used for the treatment of cancer. Although HDACis are effective in the treatment of T-cell lymphomas, treatment of solid tumors with this class of drugs has not been successful. Overexpression of the multidrug resistance protein P-glycoprotein (P-gp), encoded by ABCB1, is known to confer resistance to the HDACi romidepsin in vitro, yet increased ABCB1 expression has not been associated with resistance in patients, suggesting that other mechanisms of resistance arise in the clinic. To identify alternative mechanisms of resistance to romidepsin, we selected MCF-7 breast cancer cells with romidepsin in the presence of the P-gp inhibitor verapamil to reduce the likelihood of P-gp-mediated resistance. The resulting cell line, MCF-7 DpVp300, does not express P-gp and was found to be selectively resistant to romidepsin but not to other HDACis such as belinostat, panobinostat, or vorinostat. RNA-sequencing analysis revealed upregulation of the mRNA coding for the putative methyltransferase, METTL7A, whose paralog, METTL7B, was previously shown to methylate thiol groups on hydrogen sulfide and captopril. As romidepsin has a thiol as the zinc-binding moiety, we hypothesized that METTL7A could inactivate romidepsin and other thiol-based HDACis via methylation of the thiol group. We demonstrate that expression of METTL7A or METTL7B confers resistance to thiol-based HDACis and that both enzymes are capable of methylating thiol-containing HDACis. We thus propose that METTL7A and METTL7B confer resistance to thiol-based HDACis by methylating and inactivating the zinc-binding thiol.
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Affiliation(s)
- Robert W. Robey
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Christina M. Fitzsimmons
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Wilfried M. Guiblet
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
- Present address: Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD
| | - William J.E. Frye
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - José M. González Dalmasy
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Li Wang
- Laboratory of Receptor Biology and Gene Expression, Developmental Genomics Group, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Drake A. Russell
- Department of Medicinal Chemistry, University of Washington, Seattle, WA
| | - Lyn M. Huff
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Andrew J. Perciaccante
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Fatima Ali-Rahmani
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
- Present Address: Taiho Pharmaceutical, Princeton, NJ
| | - Crystal C. Lipsey
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Heidi M. Wade
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Allison V. Mitchell
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Siddhardha S. Maligireddy
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - David Terrero
- Department of Pharmacology and Experimental Therapeutics, Department of Cancer Cell and Cancer Biology, University of Toledo, Toledo, OH
| | - Donna Butcher
- Molecular Histopathology Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Elijah F. Edmondson
- Molecular Histopathology Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Lisa M. Jenkins
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Tatiana Nikitina
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Victor B. Zhurkin
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Amit K. Tiwari
- Department of Pharmacology and Experimental Therapeutics, Department of Cancer Cell and Cancer Biology, University of Toledo, Toledo, OH
| | | | - Rheem A. Totah
- Department of Medicinal Chemistry, University of Washington, Seattle, WA
| | - Susan E. Bates
- Division of Hematology/Oncology, Department of Medicine, Columbia University Medical Center, New York, NY and Hematology/Oncology Research Department, James J. Peters Department of Veterans Affairs Medical Center, New York, NY
| | - H. Efsun Arda
- Laboratory of Receptor Biology and Gene Expression, Developmental Genomics Group, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Michael M. Gottesman
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Pedro J. Batista
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
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23
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Andersson D, Kebede FT, Escobar M, Österlund T, Ståhlberg A. Principles of digital sequencing using unique molecular identifiers. Mol Aspects Med 2024; 96:101253. [PMID: 38367531 DOI: 10.1016/j.mam.2024.101253] [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: 11/16/2023] [Revised: 01/26/2024] [Accepted: 02/03/2024] [Indexed: 02/19/2024]
Abstract
Massively parallel sequencing technologies have long been used in both basic research and clinical routine. The recent introduction of digital sequencing has made previously challenging applications possible by significantly improving sensitivity and specificity to now allow detection of rare sequence variants, even at single molecule level. Digital sequencing utilizes unique molecular identifiers (UMIs) to minimize sequencing-induced errors and quantification biases. Here, we discuss the principles of UMIs and how they are used in digital sequencing. We outline the properties of different UMI types and the consequences of various UMI approaches in relation to experimental protocols and bioinformatics. Finally, we describe how digital sequencing can be applied in specific research fields, focusing on cancer management where it can be used in screening of asymptomatic individuals, diagnosis, treatment prediction, prognostication, monitoring treatment efficacy and early detection of treatment resistance as well as relapse.
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Affiliation(s)
- Daniel Andersson
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden
| | - Firaol Tamiru Kebede
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden
| | - Mandy Escobar
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden
| | - Tobias Österlund
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 413 90, Gothenburg, Sweden; Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Anders Ståhlberg
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 413 90, Gothenburg, Sweden; Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden.
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24
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Eisele AS, Tarbier M, Dormann AA, Pelechano V, Suter DM. Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets. Nat Commun 2024; 15:2744. [PMID: 38553478 PMCID: PMC10980719 DOI: 10.1038/s41467-024-47158-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 03/20/2024] [Indexed: 04/02/2024] Open
Abstract
Assigning single cell transcriptomes to cellular lineage trees by lineage tracing has transformed our understanding of differentiation during development, regeneration, and disease. However, lineage tracing is technically demanding, often restricted in time-resolution, and most scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational tool allowing to robustly identify small to medium-sized cell lineages solely from scRNA-seq datasets. GEMLI allows to study heritable gene expression, to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. In human breast cancer biopsies, GEMLI reveals previously unknown gene expression changes at the onset of cancer invasiveness. The universal applicability of GEMLI allows studying the role of small cell lineages in a wide range of physiological and pathological contexts, notably in vivo. GEMLI is available as an R package on GitHub ( https://github.com/UPSUTER/GEMLI ).
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Affiliation(s)
- A S Eisele
- Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland.
| | - M Tarbier
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Solna, Sweden
| | - A A Dormann
- Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland
| | - V Pelechano
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Solna, Sweden
| | - D M Suter
- Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland.
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25
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Schaff DL, Fasse AJ, White PE, Vander Velde RJ, Shaffer SM. Clonal differences underlie variable responses to sequential and prolonged treatment. Cell Syst 2024; 15:213-226.e9. [PMID: 38401539 PMCID: PMC11003565 DOI: 10.1016/j.cels.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 11/14/2023] [Accepted: 01/29/2024] [Indexed: 02/26/2024]
Abstract
Cancer cells exhibit dramatic differences in gene expression at the single-cell level, which can predict whether they become resistant to treatment. Treatment perpetuates this heterogeneity, resulting in a diversity of cell states among resistant clones. However, it remains unclear whether these differences lead to distinct responses when another treatment is applied or the same treatment is continued. In this study, we combined single-cell RNA sequencing with barcoding to track resistant clones through prolonged and sequential treatments. We found that cells within the same clone have similar gene expression states after multiple rounds of treatment. Moreover, we demonstrated that individual clones have distinct and differing fates, including growth, survival, or death, when subjected to a second treatment or when the first treatment is continued. By identifying gene expression states that predict clone survival, this work provides a foundation for selecting optimal therapies that target the most aggressive resistant clones within a tumor. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Dylan L Schaff
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19146, USA
| | - Aria J Fasse
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19146, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Phoebe E White
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19146, USA
| | - Robert J Vander Velde
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19146, USA; Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19146, USA
| | - Sydney M Shaffer
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19146, USA; Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19146, USA.
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26
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Shlyakhtina Y, Bloechl B, Moran KL, Portal MM. Protocol to study the inheritance and propagation of non-genetically encoded states using barcode decay lineage tracing. STAR Protoc 2024; 5:102809. [PMID: 38180835 PMCID: PMC10801334 DOI: 10.1016/j.xpro.2023.102809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 11/21/2023] [Accepted: 12/15/2023] [Indexed: 01/07/2024] Open
Abstract
Here, we present a protocol to perform barcode decay lineage tracing followed by single-cell transcriptome analysis (BdLT-Seq). We describe steps for BdLT-Seq experimental design, building barcoded episome reporters, performing episome transfection, and barcode retrieval. We then describe procedures for sequencing library construction while providing options for sample multiplexing and data analysis. This BdLT-Seq technique enables the assessment of clonal evolution in a directional manner while preserving isogeneity, thus allowing the comparison of non-genetic molecular features between isogenic cell lineages. For complete details on the use and execution of this protocol, please refer to Shlyakhtina et al. (2023).1.
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Affiliation(s)
- Yelyzaveta Shlyakhtina
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK
| | - Bianca Bloechl
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK
| | - Katherine L Moran
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Maximiliano M Portal
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK.
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27
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Zhang Q, Olofzon R, Konturek-Ciesla A, Yuan O, Bryder D. Ex vivo expansion potential of murine hematopoietic stem cells is a rare property only partially predicted by phenotype. eLife 2024; 12:RP91826. [PMID: 38446538 PMCID: PMC10942641 DOI: 10.7554/elife.91826] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024] Open
Abstract
The scarcity of hematopoietic stem cells (HSCs) restricts their use in both clinical settings and experimental research. Here, we examined a recently developed method for expanding rigorously purified murine HSCs ex vivo. After 3 weeks of culture, only 0.1% of cells exhibited the input HSC phenotype, but these accounted for almost all functional long-term HSC activity. Input HSCs displayed varying potential for ex vivo self-renewal, with alternative outcomes revealed by single-cell multimodal RNA and ATAC sequencing profiling. While most HSC progeny offered only transient in vivo reconstitution, these cells efficiently rescued mice from lethal myeloablation. The amplification of functional HSC activity allowed for long-term multilineage engraftment in unconditioned hosts that associated with a return of HSCs to quiescence. Thereby, our findings identify several key considerations for ex vivo HSC expansion, with major implications also for assessment of normal HSC activity.
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Affiliation(s)
- Qinyu Zhang
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund Stem Cell Center, Faculty of Medical, Lund UniversityLundSweden
| | - Rasmus Olofzon
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund Stem Cell Center, Faculty of Medical, Lund UniversityLundSweden
| | - Anna Konturek-Ciesla
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund Stem Cell Center, Faculty of Medical, Lund UniversityLundSweden
| | - Ouyang Yuan
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund Stem Cell Center, Faculty of Medical, Lund UniversityLundSweden
| | - David Bryder
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund Stem Cell Center, Faculty of Medical, Lund UniversityLundSweden
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28
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Lim B, Domsch K, Mall M, Lohmann I. Canalizing cell fate by transcriptional repression. Mol Syst Biol 2024; 20:144-161. [PMID: 38302581 PMCID: PMC10912439 DOI: 10.1038/s44320-024-00014-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/28/2023] [Accepted: 12/15/2023] [Indexed: 02/03/2024] Open
Abstract
Precision in the establishment and maintenance of cellular identities is crucial for the development of multicellular organisms and requires tight regulation of gene expression. While extensive research has focused on understanding cell type-specific gene activation, the complex mechanisms underlying the transcriptional repression of alternative fates are not fully understood. Here, we provide an overview of the repressive mechanisms involved in cell fate regulation. We discuss the molecular machinery responsible for suppressing alternative fates and highlight the crucial role of sequence-specific transcription factors (TFs) in this process. Depletion of these TFs can result in unwanted gene expression and increased cellular plasticity. We suggest that these TFs recruit cell type-specific repressive complexes to their cis-regulatory elements, enabling them to modulate chromatin accessibility in a context-dependent manner. This modulation effectively suppresses master regulators of alternative fate programs and their downstream targets. The modularity and dynamic behavior of these repressive complexes enables a limited number of repressors to canalize and maintain major and minor cell fate decisions at different stages of development.
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Affiliation(s)
- Bryce Lim
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Katrin Domsch
- Heidelberg University, Centre for Organismal Studies (COS) Heidelberg, Department of Developmental Biology and Cell Networks - Cluster of Excellence, Heidelberg, Germany
| | - Moritz Mall
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany.
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany.
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany.
| | - Ingrid Lohmann
- Heidelberg University, Centre for Organismal Studies (COS) Heidelberg, Department of Developmental Biology and Cell Networks - Cluster of Excellence, Heidelberg, Germany.
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29
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Tur S, Palii CG, Brand M. Cell fate decision in erythropoiesis: Insights from multiomics studies. Exp Hematol 2024; 131:104167. [PMID: 38262486 PMCID: PMC10939800 DOI: 10.1016/j.exphem.2024.104167] [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: 11/15/2023] [Revised: 01/10/2024] [Accepted: 01/13/2024] [Indexed: 01/25/2024]
Abstract
Every second, the body produces 2 million red blood cells through a process called erythropoiesis. Erythropoiesis is hierarchical in that it results from a series of cell fate decisions whereby hematopoietic stem cells progress toward the erythroid lineage. Single-cell transcriptomic and proteomic approaches have revolutionized the way we understand erythropoiesis, revealing it to be a gradual process that underlies a progressive restriction of fate potential driven by quantitative changes in lineage-specifying transcription factors. Despite these major advances, we still know very little about what cell fate decision entails at the molecular level. Novel approaches that simultaneously measure additional properties in single cells, including chromatin accessibility, transcription factor binding, and/or cell surface proteins are being developed at a fast pace, providing the means to exciting new advances in the near future. In this review, we briefly summarize the main findings obtained from single-cell studies of erythropoiesis, highlight outstanding questions, and suggest recent technological advances to address them.
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Affiliation(s)
- Steven Tur
- Department of Cell and Regenerative Biology, Wisconsin Blood Cancer Research Institute, Wisconsin Institutes for Medical Research, University of Wisconsin School of Medicine and Public Health, Carbone Cancer Center, Madison, WI; Cellular and Molecular Biology Graduate Program, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Carmen G Palii
- Department of Cell and Regenerative Biology, Wisconsin Blood Cancer Research Institute, Wisconsin Institutes for Medical Research, University of Wisconsin School of Medicine and Public Health, Carbone Cancer Center, Madison, WI
| | - Marjorie Brand
- Department of Cell and Regenerative Biology, Wisconsin Blood Cancer Research Institute, Wisconsin Institutes for Medical Research, University of Wisconsin School of Medicine and Public Health, Carbone Cancer Center, Madison, WI.
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30
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Jain N, Goyal Y, Dunagin MC, Cote CJ, Mellis IA, Emert B, Jiang CL, Dardani IP, Reffsin S, Arnett M, Yang W, Raj A. Retrospective identification of cell-intrinsic factors that mark pluripotency potential in rare somatic cells. Cell Syst 2024; 15:109-133.e10. [PMID: 38335955 PMCID: PMC10940218 DOI: 10.1016/j.cels.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/31/2023] [Accepted: 01/12/2024] [Indexed: 02/12/2024]
Abstract
Pluripotency can be induced in somatic cells by the expression of OCT4, KLF4, SOX2, and MYC. Usually only a rare subset of cells reprogram, and the molecular characteristics of this subset remain unknown. We apply retrospective clone tracing to identify and characterize the rare human fibroblasts primed for reprogramming. These fibroblasts showed markers of increased cell cycle speed and decreased fibroblast activation. Knockdown of a fibroblast activation factor identified by our analysis increased the reprogramming efficiency. We provide evidence for a unified model in which cells can move into and out of the primed state over time, explaining how reprogramming appears deterministic at short timescales and stochastic at long timescales. Furthermore, inhibiting the activity of LSD1 enlarged the pool of cells that were primed for reprogramming. Thus, even homogeneous cell populations can exhibit heritable molecular variability that can dictate whether individual rare cells will reprogram or not.
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Affiliation(s)
- Naveen Jain
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL 60611, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Margaret C Dunagin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christopher J Cote
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ian A Mellis
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Benjamin Emert
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Connie L Jiang
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ian P Dardani
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sam Reffsin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Miles Arnett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wenli Yang
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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31
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Dai J, Zheng S, Falco MM, Bao J, Eriksson J, Pikkusaari S, Forstén S, Jiang J, Wang W, Gao L, Perez-Villatoro F, Dufva O, Saeed K, Wang Y, Amiryousefi A, Färkkilä A, Mustjoki S, Kauppi L, Tang J, Vähärautio A. Tracing back primed resistance in cancer via sister cells. Nat Commun 2024; 15:1158. [PMID: 38326354 PMCID: PMC10850087 DOI: 10.1038/s41467-024-45478-7] [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: 04/25/2023] [Accepted: 01/24/2024] [Indexed: 02/09/2024] Open
Abstract
Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. Here, we present ReSisTrace that uses shared transcriptomic features of sister cells to predict the states priming treatment resistance. Applying ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells reveals pre-resistant phenotypes defined by proteostatic and mRNA surveillance features, reflecting traits enriched in the upcoming subclonal selection. Furthermore, we show that DNA repair deficiency renders cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leverage the obtained pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.
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Affiliation(s)
- Jun Dai
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Shuyu Zheng
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Matías M Falco
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jie Bao
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johanna Eriksson
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sanna Pikkusaari
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sofia Forstén
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jing Jiang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Wenyu Wang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Luping Gao
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Fernando Perez-Villatoro
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine, Helsinki, Finland
| | - Olli Dufva
- Research Program in Translational Immunology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Khalid Saeed
- Research Program in Translational Immunology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Yinyin Wang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ali Amiryousefi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Anniina Färkkilä
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, and Clinical Trial Unit, Comprehensive Cancer Centre, Helsinki University Hospital, Helsinki, Finland
| | - Satu Mustjoki
- Research Program in Translational Immunology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Liisa Kauppi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Anna Vähärautio
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Foundation for the Finnish Cancer Institute, Helsinki, Finland.
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32
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Sun W, Perkins M, Huyghe M, Faraldo MM, Fre S, Perié L, Lyne AM. Extracting, filtering and simulating cellular barcodes using CellBarcode tools. NATURE COMPUTATIONAL SCIENCE 2024; 4:128-143. [PMID: 38374363 PMCID: PMC10899113 DOI: 10.1038/s43588-024-00595-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/16/2024] [Indexed: 02/21/2024]
Abstract
Identifying true DNA cellular barcodes among polymerase chain reaction and sequencing errors is challenging. Current tools are restricted in the diversity of barcode types supported or the analysis strategies implemented. As such, there is a need for more versatile and efficient tools for barcode extraction, as well as for tools to investigate which factors impact barcode detection and which filtering strategies to best apply. Here we introduce the package CellBarcode and its barcode simulation kit, CellBarcodeSim, that allows efficient and versatile barcode extraction and filtering for a range of barcode types from bulk or single-cell sequencing data using a variety of filtering strategies. Using the barcode simulation kit and biological data, we explore the technical and biological factors influencing barcode identification and provide a decision tree on how to optimize barcode identification for different barcode settings. We believe that CellBarcode and CellBarcodeSim have the capability to enhance the reproducibility and interpretation of barcode results across studies.
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Affiliation(s)
- Wenjie Sun
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
| | - Meghan Perkins
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Mathilde Huyghe
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Marisa M Faraldo
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Silvia Fre
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Leïla Perié
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
| | - Anne-Marie Lyne
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
- INSERM U900, Paris, France.
- MINES ParisTech, CBIO-Centre for Computational Biology, PSL Research University, Paris, France.
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33
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Zhu Q, Conrad DN, Gartner ZJ. deMULTIplex2: robust sample demultiplexing for scRNA-seq. Genome Biol 2024; 25:37. [PMID: 38291503 PMCID: PMC10829271 DOI: 10.1186/s13059-024-03177-y] [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: 04/27/2023] [Accepted: 01/18/2024] [Indexed: 02/01/2024] Open
Abstract
Sample multiplexing enables pooled analysis during single-cell RNA sequencing workflows, thereby increasing throughput and reducing batch effects. A challenge for all multiplexing techniques is to link sample-specific barcodes with cell-specific barcodes, then demultiplex sample identity post-sequencing. However, existing demultiplexing tools fail under many real-world conditions where barcode cross-contamination is an issue. We therefore developed deMULTIplex2, an algorithm inspired by a mechanistic model of barcode cross-contamination. deMULTIplex2 employs generalized linear models and expectation-maximization to probabilistically determine the sample identity of each cell. Benchmarking reveals superior performance across various experimental conditions, particularly on large or noisy datasets with unbalanced sample compositions.
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Affiliation(s)
- Qin Zhu
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA.
| | - Daniel N Conrad
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
- Center for Cellular Construction, University of California, San Francisco, CA, 94158, USA.
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34
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Li Z, Yang W, Wu P, Shan Y, Zhang X, Chen F, Yang J, Yang JR. Reconstructing cell lineage trees with genomic barcoding: approaches and applications. J Genet Genomics 2024; 51:35-47. [PMID: 37269980 DOI: 10.1016/j.jgg.2023.05.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/19/2023] [Accepted: 05/20/2023] [Indexed: 06/05/2023]
Abstract
In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.
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Affiliation(s)
- Zizhang Li
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Wenjing Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Peng Wu
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuyan Shan
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xiaoyu Zhang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Feng Chen
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Junnan Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jian-Rong Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
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35
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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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36
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Kim IS. DNA Barcoding Technology for Lineage Recording and Tracing to Resolve Cell Fate Determination. Cells 2023; 13:27. [PMID: 38201231 PMCID: PMC10778210 DOI: 10.3390/cells13010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
In various biological contexts, cells receive signals and stimuli that prompt them to change their current state, leading to transitions into a future state. This change underlies the processes of development, tissue maintenance, immune response, and the pathogenesis of various diseases. Following the path of cells from their initial identity to their current state reveals how cells adapt to their surroundings and undergo transformations to attain adjusted cellular states. DNA-based molecular barcoding technology enables the documentation of a phylogenetic tree and the deterministic events of cell lineages, providing the mechanisms and timing of cell lineage commitment that can either promote homeostasis or lead to cellular dysregulation. This review comprehensively presents recently emerging molecular recording technologies that utilize CRISPR/Cas systems, base editing, recombination, and innate variable sequences in the genome. Detailing their underlying principles, applications, and constraints paves the way for the lineage tracing of every cell within complex biological systems, encompassing the hidden steps and intermediate states of organism development and disease progression.
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Affiliation(s)
- Ik Soo Kim
- Department of Microbiology, Gachon University College of Medicine, Incheon 21999, Republic of Korea
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37
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Schaukowitch K, Janas JA, Wernig M. Insights and applications of direct neuronal reprogramming. Curr Opin Genet Dev 2023; 83:102128. [PMID: 37862835 PMCID: PMC11335363 DOI: 10.1016/j.gde.2023.102128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/07/2023] [Accepted: 09/19/2023] [Indexed: 10/22/2023]
Abstract
Direct neuronal reprogramming converts somatic cells of a defined lineage into induced neuronal cells without going through a pluripotent intermediate. This approach not only provides access to the otherwise largely inaccessible cells of the brain for neuronal disease modeling, but also holds great promise for ultimately enabling neuronal cell replacement without the use of transplantation. To improve efficiency and specificity of direct neuronal reprogramming, much of the current efforts aim to understand the mechanisms that safeguard cell identities and how the reprogramming cells overcome the barriers resisting fate changes. Here, we review recent discoveries into the mechanisms by which the donor cell program is silenced, and new cell identities are established. We also discuss advancements that have been made toward fine-tuning the output of these reprogramming systems to generate specific types of neuronal cells. Finally, we highlight the benefit of using direct neuronal reprogramming to study age-related disorders and the potential of in vivo direct reprogramming in regenerative medicine.
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Affiliation(s)
- Katie Schaukowitch
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Justyna A Janas
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Marius Wernig
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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38
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Li L, Bowling S, McGeary SE, Yu Q, Lemke B, Alcedo K, Jia Y, Liu X, Ferreira M, Klein AM, Wang SW, Camargo FD. A mouse model with high clonal barcode diversity for joint lineage, transcriptomic, and epigenomic profiling in single cells. Cell 2023; 186:5183-5199.e22. [PMID: 37852258 DOI: 10.1016/j.cell.2023.09.019] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 07/11/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023]
Abstract
Cellular lineage histories and their molecular states encode fundamental principles of tissue development and homeostasis. Current lineage-recording mouse models have insufficient barcode diversity and single-cell lineage coverage for profiling tissues composed of millions of cells. Here, we developed DARLIN, an inducible Cas9 barcoding mouse line that utilizes terminal deoxynucleotidyl transferase (TdT) and 30 CRISPR target sites. DARLIN is inducible, generates massive lineage barcodes across tissues, and enables the detection of edited barcodes in ∼70% of profiled single cells. Using DARLIN, we examined fate bias within developing hematopoietic stem cells (HSCs) and revealed unique features of HSC migration. Additionally, we established a protocol for joint transcriptomic and epigenomic single-cell measurements with DARLIN and found that cellular clonal memory is associated with genome-wide DNA methylation rather than gene expression or chromatin accessibility. DARLIN will enable the high-resolution study of lineage relationships and their molecular signatures in diverse tissues and physiological contexts.
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Affiliation(s)
- Li Li
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sarah Bowling
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sean E McGeary
- Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Qi Yu
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Bianca Lemke
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Karel Alcedo
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Yuemeng Jia
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Xugeng Liu
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Mark Ferreira
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Allon M Klein
- Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Shou-Wen Wang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; School of Science, Westlake University, Hangzhou, Zhejiang 310024, China.
| | - Fernando D Camargo
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
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39
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Harmange G, Hueros RAR, Schaff DL, Emert B, Saint-Antoine M, Kim LC, Niu Z, Nellore S, Fane ME, Alicea GM, Weeraratna AT, Simon MC, Singh A, Shaffer SM. Disrupting cellular memory to overcome drug resistance. Nat Commun 2023; 14:7130. [PMID: 37932277 PMCID: PMC10628298 DOI: 10.1038/s41467-023-41811-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 09/15/2023] [Indexed: 11/08/2023] Open
Abstract
Gene expression states persist for varying lengths of time at the single-cell level, a phenomenon known as gene expression memory. When cells switch states, losing memory of their prior state, this transition can occur in the absence of genetic changes. However, we lack robust methods to find regulators of memory or track state switching. Here, we develop a lineage tracing-based technique to quantify memory and identify cells that switch states. Applied to melanoma cells without therapy, we quantify long-lived fluctuations in gene expression that are predictive of later resistance to targeted therapy. We also identify the PI3K and TGF-β pathways as state switching modulators. We propose a pretreatment model, first applying a PI3K inhibitor to modulate gene expression states, then applying targeted therapy, which leads to less resistance than targeted therapy alone. Together, we present a method for finding modulators of gene expression memory and their associated cell fates.
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Affiliation(s)
- Guillaume Harmange
- Cellular and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raúl A Reyes Hueros
- Department of Biochemistry and Molecular Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dylan L Schaff
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin Emert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Michael Saint-Antoine
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, 19716, USA
| | - Laura C Kim
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zijian Niu
- Department of Chemistry, College of the Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics, College of the Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Shivani Nellore
- Department of Biology, College of the Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Mitchell E Fane
- Cancer Signaling and Microenvironment Research Program, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Gretchen M Alicea
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Ashani T Weeraratna
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - M Celeste Simon
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, 19716, USA
| | - Sydney M Shaffer
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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40
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Cotton JL, Estrada Diez J, Sagar V, Chen J, Piquet M, Alford J, Song Y, Li X, Riester M, DiMare MT, Schumacher K, Boulay G, Sprouffske K, Fan L, Burks T, Mansur L, Wagner J, Bhang HEC, Iartchouk O, Reece-Hoyes J, Morris EJ, Hammerman PS, Ruddy DA, Korn JM, Engelman JA, Niederst MJ. Expressed Barcoding Enables High-Resolution Tracking of the Evolution of Drug Tolerance. Cancer Res 2023; 83:3611-3623. [PMID: 37603596 DOI: 10.1158/0008-5472.can-23-0144] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/11/2023] [Accepted: 08/15/2023] [Indexed: 08/23/2023]
Abstract
For a majority of patients with non-small cell lung cancer with EGFR mutations, treatment with EGFR inhibitors (EGFRi) induces a clinical response. Despite this initial reduction in tumor size, residual disease persists that leads to disease relapse. Elucidating the preexisting biological differences between sensitive cells and surviving drug-tolerant persister cells and deciphering how drug-tolerant cells evolve in response to treatment could help identify strategies to improve the efficacy of EGFRi. In this study, we tracked the origins and clonal evolution of drug-tolerant cells at a high resolution by using an expressed barcoding system coupled with single-cell RNA sequencing. This platform enabled longitudinal profiling of gene expression and drug sensitivity in response to EGFRi across a large number of clones. Drug-tolerant cells had higher expression of key survival pathways such as YAP and EMT at baseline and could also differentially adapt their gene expression following EGFRi treatment compared with sensitive cells. In addition, drug combinations targeting common downstream components (MAPK) or orthogonal factors (chemotherapy) showed greater efficacy than EGFRi alone, which is attributable to broader targeting of the heterogeneous EGFRi-tolerance mechanisms present in tumors. Overall, this approach facilitates thorough examination of clonal evolution in response to therapy that could inform the development of improved diagnostic approaches and treatment strategies for targeting drug-tolerant cells. SIGNIFICANCE The evolution and heterogeneity of EGFR inhibitor tolerance are identified in a large number of clones at enhanced cellular and temporal resolution using an expressed barcode technology coupled with single-cell RNA sequencing.
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Affiliation(s)
- Jennifer L Cotton
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Javier Estrada Diez
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Vivek Sagar
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Julie Chen
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Michelle Piquet
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - John Alford
- Chemical Biology & Therapeutics, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Youngchul Song
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Xiaoyan Li
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Markus Riester
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Matthew T DiMare
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Katja Schumacher
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Gaylor Boulay
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Kathleen Sprouffske
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Lin Fan
- Chemical Biology & Therapeutics, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Tyler Burks
- Chemical Biology & Therapeutics, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Leandra Mansur
- Chemical Biology & Therapeutics, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Joel Wagner
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Hyo-Eun C Bhang
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Oleg Iartchouk
- Chemical Biology & Therapeutics, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - John Reece-Hoyes
- Chemical Biology & Therapeutics, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Erick J Morris
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Peter S Hammerman
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - David A Ruddy
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Joshua M Korn
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Jeffrey A Engelman
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Matthew J Niederst
- Oncology Disease Area, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
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41
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Liu Y, Wang J, Südhof TC, Wernig M. Efficient generation of functional neurons from mouse embryonic stem cells via neurogenin-2 expression. Nat Protoc 2023; 18:2954-2974. [PMID: 37596357 PMCID: PMC11349042 DOI: 10.1038/s41596-023-00863-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 04/27/2023] [Indexed: 08/20/2023]
Abstract
The production of induced neuronal (iN) cells from human embryonic stem cells (ESCs) and induced pluripotent stem cells by the forced expression of proneural transcription factors is rapid, efficient and reproducible. The ability to generate large numbers of human neurons in such a robust manner enables large-scale studies of human neural differentiation and neuropsychiatric diseases. Surprisingly, similar transcription factor-based approaches for converting mouse ESCs into iN cells have been challenging, primarily because of low cell survival. Here, we provide a detailed approach for the efficient and reproducible generation of functional iN cells from mouse ESC cultures by the genetically induced expression of neurogenin-2. The resulting iN cells display mature pre- and postsynaptic specializations and form synaptic networks. Our method provides the basis for studying neuronal development and enables the direct comparison of cellular phenotypes in mouse and human neurons generated in an equivalent way. The procedure requires 14 d and can be carried out by users with expertise in stem cell culture.
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Affiliation(s)
- Yingfei Liu
- Institute for Stem Cell Biology and Regenerative Medicine, Departments of Pathology and Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Shaanxi Provincial People's Hospital, Xi'an, China
| | - Jinzhao Wang
- Institute for Stem Cell Biology and Regenerative Medicine, Departments of Pathology and Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Molecular & Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Thomas C Südhof
- Department of Molecular & Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Marius Wernig
- Institute for Stem Cell Biology and Regenerative Medicine, Departments of Pathology and Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
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42
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Han L, Song B, Zhang P, Zhong Z, Zhang Y, Bo X, Wang H, Zhang Y, Cui X, Zhou W. PC3T: a signature-driven predictor of chemical compounds for cellular transition. Commun Biol 2023; 6:989. [PMID: 37758874 PMCID: PMC10533498 DOI: 10.1038/s42003-023-05225-y] [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: 09/29/2022] [Accepted: 08/07/2023] [Indexed: 09/29/2023] Open
Abstract
Cellular transitions hold great promise in translational medicine research. However, therapeutic applications are limited by the low efficiency and safety concerns of using transcription factors. Small molecules provide a temporal and highly tunable approach to overcome these issues. Here, we present PC3T, a computational framework to enrich molecules that induce desired cellular transitions, and PC3T was able to consistently enrich small molecules that had been experimentally validated in both bulk and single-cell datasets. We then predicted small molecule reprogramming of fibroblasts into hepatic progenitor-like cells (HPLCs). The converted cells exhibited epithelial cell-like morphology and HPLC-like gene expression pattern. Hepatic functions were also observed, such as glycogen storage and lipid accumulation. Finally, we collected and manually curated a cell state transition resource containing 224 time-course gene expression datasets and 153 cell types. Our framework, together with the data resource, is freely available at http://pc3t.idrug.net.cn/ . We believe that PC3T is a powerful tool to promote chemical-induced cell state transitions.
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Affiliation(s)
- Lu Han
- Beijing Institute of Pharmacology and Toxicology, 100850, Beijing, China
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, China
| | - Bin Song
- Department of Pancreatic Surgery, Changhai Hospital, Second Military Medical University, 200438, Shanghai, China
| | - Peilin Zhang
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, 200438, Shanghai, China
| | - Zhi Zhong
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Yongxiang Zhang
- Beijing Institute of Pharmacology and Toxicology, 100850, Beijing, China
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, China
| | - Xiaochen Bo
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, 100850, Beijing, China
| | - Hongyang Wang
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, 200438, Shanghai, China
| | - Yong Zhang
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, China.
| | - Xiuliang Cui
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, 200438, Shanghai, China.
| | - Wenxia Zhou
- Beijing Institute of Pharmacology and Toxicology, 100850, Beijing, China.
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, China.
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43
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Farrell S, Mani M, Goyal S. Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics. CELL REPORTS METHODS 2023; 3:100581. [PMID: 37708894 PMCID: PMC10545944 DOI: 10.1016/j.crmeth.2023.100581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 06/16/2023] [Accepted: 08/18/2023] [Indexed: 09/16/2023]
Abstract
Gene expression dynamics provide directional information for trajectory inference from single-cell RNA sequencing data. Traditional approaches compute RNA velocity using strict modeling assumptions about transcription and splicing of RNA. This can fail in scenarios where multiple lineages have distinct gene dynamics or where rates of transcription and splicing are time dependent. We present "LatentVelo," an approach to compute a low-dimensional representation of gene dynamics with deep learning. LatentVelo embeds cells into a latent space with a variational autoencoder and models differentiation dynamics on this "dynamics-based" latent space with neural ordinary differential equations. LatentVelo infers a latent regulatory state that controls the dynamics of an individual cell to model multiple lineages. LatentVelo can predict latent trajectories, describing the inferred developmental path for individual cells rather than just local RNA velocity vectors. The dynamics-based embedding batch corrects cell states and velocities, outperforming comparable autoencoder batch correction methods that do not consider gene expression dynamics.
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Affiliation(s)
- Spencer Farrell
- Department of Physics, University of Toronto, Toronto, ON M5S1A7, Canada.
| | - Madhav Mani
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA; NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA; Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
| | - Sidhartha Goyal
- Department of Physics, University of Toronto, Toronto, ON M5S1A7, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S3G9, Canada.
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44
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Fei L, Zhang K, Poddar N, Hautaniemi S, Sahu B. Single-cell epigenome analysis identifies molecular events controlling direct conversion of human fibroblasts to pancreatic ductal-like cells. Dev Cell 2023; 58:1701-1715.e8. [PMID: 37751683 DOI: 10.1016/j.devcel.2023.08.023] [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: 03/08/2023] [Revised: 07/13/2023] [Accepted: 08/16/2023] [Indexed: 09/28/2023]
Abstract
Cell fate can be reprogrammed by ectopic expression of lineage-specific transcription factors (TFs). However, the exact cell state transitions during transdifferentiation are still poorly understood. Here, we have generated pancreatic exocrine cells of ductal epithelial identity from human fibroblasts using a set of six TFs. We mapped the molecular determinants of lineage dynamics using a factor-indexing method based on single-nuclei multiome sequencing (FI-snMultiome-seq) that enables dissecting the role of each individual TF and pool of TFs in cell fate conversion. We show that transition from mesenchymal fibroblast identity to epithelial pancreatic exocrine fate involves two deterministic steps: an endodermal progenitor state defined by activation of HHEX with FOXA2 and SOX17 and a temporal GATA4 activation essential for the maintenance of pancreatic cell fate program. Collectively, our data suggest that transdifferentiation-although being considered a direct cell fate conversion method-occurs through transient progenitor states orchestrated by stepwise activation of distinct TFs.
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Affiliation(s)
- Liangru Fei
- Applied Tumor Genomics Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, Helsinki 00014, Finland
| | - Kaiyang Zhang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, Helsinki 00014, Finland
| | - Nikita Poddar
- Applied Tumor Genomics Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, Helsinki 00014, Finland
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, Helsinki 00014, Finland
| | - Biswajyoti Sahu
- Applied Tumor Genomics Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, Helsinki 00014, Finland; iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Haartmaninkatu 8, Helsinki 00014, Finland; Medicum, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, Helsinki 00014, Finland; Centre for Molecular Medicine Norway, Faculty of Medicine, University of Oslo, Gaustadelléen 21, 0349 Oslo, Norway.
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45
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Lu X, Lofgren SM, Zhao Y, Mazur PK. Multiplexed transcriptomic profiling of the fate of human CAR T cells in vivo via genetic barcoding with shielded small nucleotides. Nat Biomed Eng 2023; 7:1170-1187. [PMID: 37652986 DOI: 10.1038/s41551-023-01085-3] [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: 09/12/2022] [Accepted: 08/01/2023] [Indexed: 09/02/2023]
Abstract
The design of chimeric antigen receptor (CAR) T cells would benefit from knowledge of the fate of the cells in vivo. This requires the permanent labelling of CAR T cell products and their pooling in the same microenvironment. Here, we report a cell-barcoding method for the multiplexed longitudinal profiling of cells in vivo using single-cell RNA sequencing (scRNA-seq). The method, which we named shielded-small-nucleotide-based scRNA-seq (SSN-seq), is compatible with both 3' and 5' single-cell profiling, and enables the recording of cell identity, from cell infusion to isolation, by leveraging the ubiquitous Pol III U6 promoters to robustly express small-RNA barcodes modified with direct-capture sequences. By using SSN-seq to track the dynamics of the states of CAR T cells in a tumour-rechallenge mouse model of leukaemia, we found that a combination of cytokines and small-molecule inhibitors that are used in the ex vivo manufacturing of CAR T cells promotes the in vivo expansion of persistent populations of CD4+ memory T cells. By facilitating the probing of cell-state dynamics in vivo, SSN-seq may aid the development of adoptive cell therapies.
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Affiliation(s)
- Xiaoyin Lu
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shane M Lofgren
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yuehui Zhao
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Pawel K Mazur
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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46
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Cannell IG, Sawicka K, Pearsall I, Wild SA, Deighton L, Pearsall SM, Lerda G, Joud F, Khan S, Bruna A, Simpson KL, Mulvey CM, Nugent F, Qosaj F, Bressan D, Dive C, Caldas C, Hannon GJ. FOXC2 promotes vasculogenic mimicry and resistance to anti-angiogenic therapy. Cell Rep 2023; 42:112791. [PMID: 37499655 DOI: 10.1016/j.celrep.2023.112791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 05/09/2022] [Accepted: 06/26/2023] [Indexed: 07/29/2023] Open
Abstract
Vasculogenic mimicry (VM) describes the formation of pseudo blood vessels constructed of tumor cells that have acquired endothelial-like properties. VM channels endow the tumor with a tumor-derived vascular system that directly connects to host blood vessels, and their presence is generally associated with poor patient prognosis. Here we show that the transcription factor, Foxc2, promotes VM in diverse solid tumor types by driving ectopic expression of endothelial genes in tumor cells, a process that is stimulated by hypoxia. VM-proficient tumors are resistant to anti-angiogenic therapy, and suppression of Foxc2 augments response. This work establishes co-option of an embryonic endothelial transcription factor by tumor cells as a key mechanism driving VM proclivity and motivates the search for VM-inhibitory agents that could form the basis of combination therapies with anti-angiogenics.
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Affiliation(s)
- Ian G Cannell
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; New York Genome Center, 101 Avenue of the Americas, New York, NY 10013, USA.
| | - Kirsty Sawicka
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; New York Genome Center, 101 Avenue of the Americas, New York, NY 10013, USA
| | - Isabella Pearsall
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; New York Genome Center, 101 Avenue of the Americas, New York, NY 10013, USA
| | - Sophia A Wild
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Lauren Deighton
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Sarah M Pearsall
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; Cancer Research UK Cancer Biomarker Centre, Manchester M20 4BX, UK; CRUK Manchester Institute, Manchester M20 4BX, UK
| | - Giulia Lerda
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Fadwa Joud
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Showkhin Khan
- New York Genome Center, 101 Avenue of the Americas, New York, NY 10013, USA
| | - Alejandra Bruna
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; Preclinical Modelling of Paediatric Cancer Evolution Team, The Institute of Cancer Research, Cotswold Road, Sutton, Surrey SM2 5N, UK
| | - Kathryn L Simpson
- Cancer Research UK Cancer Biomarker Centre, Manchester M20 4BX, UK; CRUK Manchester Institute, Manchester M20 4BX, UK
| | - Claire M Mulvey
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Fiona Nugent
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Fatime Qosaj
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Dario Bressan
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Caroline Dive
- Cancer Research UK Cancer Biomarker Centre, Manchester M20 4BX, UK; CRUK Manchester Institute, Manchester M20 4BX, UK
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; Department of Oncology and Breast Cancer Programme, CRUK Cambridge Centre, Cambridge University Hospitals NHS and University of Cambridge, Cambridge CB2 2QQ, UK
| | - Gregory J Hannon
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; New York Genome Center, 101 Avenue of the Americas, New York, NY 10013, USA.
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47
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Peng YR. Cell-type specification in the retina: Recent discoveries from transcriptomic approaches. Curr Opin Neurobiol 2023; 81:102752. [PMID: 37499619 DOI: 10.1016/j.conb.2023.102752] [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: 05/16/2023] [Revised: 06/26/2023] [Accepted: 06/29/2023] [Indexed: 07/29/2023]
Abstract
Understanding the formation of the complex nervous system hinges on decoding the mechanism that specifies a vast array of neuronal types, each endowed with a unique morphology, physiology, and connectivity. As a pivotal step towards addressing this problem, seminal work has been devoted to characterizing distinct neuronal types. In recent years, high-throughput, single-cell transcriptomic methods have enabled a rapid inventory of cell types in various regions of the nervous system, with the retina exhibiting complete molecular characterization across many vertebrate species. This invaluable resource has furnished a fresh perspective for investigating the molecular principles of cell-type specification, thereby advancing our understanding of retinal development. Accordingly, this review focuses on the most recent transcriptomic characterizations of retinal cells, with a particular focus on amacrine cells and retinal ganglion cells. These investigations have unearthed new insights into their cell-type specification.
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Affiliation(s)
- Yi-Rong Peng
- Department of Ophthalmology and Stein Eye Institute, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA.
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48
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Xie L, Liu H, You Z, Wang L, Li Y, Zhang X, Ji X, He H, Yuan T, Zheng W, Wu Z, Xiong M, Wei W, Chen Y. Comprehensive spatiotemporal mapping of single-cell lineages in developing mouse brain by CRISPR-based barcoding. Nat Methods 2023; 20:1244-1255. [PMID: 37460718 DOI: 10.1038/s41592-023-01947-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 06/06/2023] [Indexed: 08/09/2023]
Abstract
A fundamental interest in developmental neuroscience lies in the ability to map the complete single-cell lineages within the brain. To this end, we developed a CRISPR editing-based lineage-specific tracing (CREST) method for clonal tracing in Cre mice. We then used two complementary strategies based on CREST to map single-cell lineages in developing mouse ventral midbrain (vMB). By applying snapshotting CREST (snapCREST), we constructed a spatiotemporal lineage landscape of developing vMB and identified six progenitor archetypes that could represent the principal clonal fates of individual vMB progenitors and three distinct clonal lineages in the floor plate that specified glutamatergic, dopaminergic or both neurons. We further created pandaCREST (progenitor and derivative associating CREST) to associate the transcriptomes of progenitor cells in vivo with their differentiation potentials. We identified multiple origins of dopaminergic neurons and demonstrated that a transcriptome-defined progenitor type comprises heterogeneous progenitors, each with distinct clonal fates and molecular signatures. Therefore, the CREST method and strategies allow comprehensive single-cell lineage analysis that could offer new insights into the molecular programs underlying neural specification.
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Affiliation(s)
- Lianshun Xie
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hengxin Liu
- University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Zhiwen You
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Luyue Wang
- University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Yiwen Li
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Xinyue Zhang
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoshan Ji
- Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Hui He
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Tingli Yuan
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Wenping Zheng
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Ziyan Wu
- UniXell Biotechnology, Shanghai, China
| | - Man Xiong
- State Key Laboratory of Medical Neurobiology-Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Wu Wei
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
- Center for Biomedical Informatics, Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
- Lingang Laboratory, Shanghai, China.
| | - Yuejun Chen
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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49
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Goyal Y, Busch GT, Pillai M, Li J, Boe RH, Grody EI, Chelvanambi M, Dardani IP, Emert B, Bodkin N, Braun J, Fingerman D, Kaur A, Jain N, Ravindran PT, Mellis IA, Kiani K, Alicea GM, Fane ME, Ahmed SS, Li H, Chen Y, Chai C, Kaster J, Witt RG, Lazcano R, Ingram DR, Johnson SB, Wani K, Dunagin MC, Lazar AJ, Weeraratna AT, Wargo JA, Herlyn M, Raj A. Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells. Nature 2023; 620:651-659. [PMID: 37468627 PMCID: PMC10628994 DOI: 10.1038/s41586-023-06342-8] [Citation(s) in RCA: 61] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Even among genetically identical cancer cells, resistance to therapy frequently emerges from a small subset of those cells1-7. Molecular differences in rare individual cells in the initial population enable certain cells to become resistant to therapy7-9; however, comparatively little is known about the variability in the resistance outcomes. Here we develop and apply FateMap, a framework that combines DNA barcoding with single-cell RNA sequencing, to reveal the fates of hundreds of thousands of clones exposed to anti-cancer therapies. We show that resistant clones emerging from single-cell-derived cancer cells adopt molecularly, morphologically and functionally distinct resistant types. These resistant types are largely predetermined by molecular differences between cells before drug addition and not by extrinsic factors. Changes in the dose and type of drug can switch the resistant type of an initial cell, resulting in the generation and elimination of certain resistant types. Samples from patients show evidence for the existence of these resistant types in a clinical context. We observed diversity in resistant types across several single-cell-derived cancer cell lines and cell types treated with a variety of drugs. The diversity of resistant types as a result of the variability in intrinsic cell states may be a generic feature of responses to external cues.
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Affiliation(s)
- Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.
| | - Gianna T Busch
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Maalavika Pillai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jingxin Li
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ryan H Boe
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emanuelle I Grody
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Manoj Chelvanambi
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ian P Dardani
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin Emert
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas Bodkin
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jonas Braun
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Amanpreet Kaur
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Naveen Jain
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pavithran T Ravindran
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian A Mellis
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Karun Kiani
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gretchen M Alicea
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Mitchell E Fane
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Syeda Subia Ahmed
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Haiyin Li
- The Wistar Institute, Philadelphia, PA, USA
| | | | - Cedric Chai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Reproductive Science, Northwestern University, Chicago, IL, USA
| | | | - Russell G Witt
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rossana Lazcano
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Davis R Ingram
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sarah B Johnson
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Khalida Wani
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Margaret C Dunagin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexander J Lazar
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ashani T Weeraratna
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jennifer A Wargo
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Guldenpfennig C, Teixeiro E, Daniels M. NF-kB's contribution to B cell fate decisions. Front Immunol 2023; 14:1214095. [PMID: 37533858 PMCID: PMC10391175 DOI: 10.3389/fimmu.2023.1214095] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/03/2023] [Indexed: 08/04/2023] Open
Abstract
NF-κB signaling is essential to an effective innate and adaptive immune response. Many immune-specific functional and developmental outcomes depend in large on NF-κB. The formidable task of sorting out the mechanisms behind the regulation and outcome of NF-κB signaling remains an important area of immunology research. Here we briefly discuss the role of NF-κB in regulating cell fate decisions at various times in the path of B cell development, activation, and the generation of long-term humoral immunity.
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Affiliation(s)
- Caitlyn Guldenpfennig
- Molecular Microbiology and Immunology, University of Missouri, Columbia, MO, United States
- NextGen Precision Health, University of Missouri, Columbia, MO, United States
| | - Emma Teixeiro
- Molecular Microbiology and Immunology, University of Missouri, Columbia, MO, United States
- NextGen Precision Health, University of Missouri, Columbia, MO, United States
| | - Mark Daniels
- Molecular Microbiology and Immunology, University of Missouri, Columbia, MO, United States
- NextGen Precision Health, University of Missouri, Columbia, MO, United States
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