1
|
Meyers S, Demeyer S, Cools J. CRISPR screening in hematology research: from bulk to single-cell level. J Hematol Oncol 2023; 16:107. [PMID: 37875911 PMCID: PMC10594891 DOI: 10.1186/s13045-023-01495-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/21/2023] [Indexed: 10/26/2023] Open
Abstract
The CRISPR genome editing technology has revolutionized the way gene function is studied. Genome editing can be achieved in single genes or for thousands of genes simultaneously in sensitive genetic screens. While conventional genetic screens are limited to bulk measurements of cell behavior, recent developments in single-cell technologies make it possible to combine CRISPR screening with single-cell profiling. In this way, cell behavior and gene expression can be monitored simultaneously, with the additional possibility of including data on chromatin accessibility and protein levels. Moreover, the availability of various Cas proteins leading to inactivation, activation, or other effects on gene function further broadens the scope of such screens. The integration of single-cell multi-omics approaches with CRISPR screening open the path to high-content information on the impact of genetic perturbations at single-cell resolution. Current limitations in cell throughput and data density need to be taken into consideration, but new technologies are rapidly evolving and are likely to easily overcome these limitations. In this review, we discuss the use of bulk CRISPR screening in hematology research, as well as the emergence of single-cell CRISPR screening and its added value to the field.
Collapse
Affiliation(s)
- Sarah Meyers
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Sofie Demeyer
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Jan Cools
- Center for Human Genetics, KU Leuven, Leuven, Belgium.
- Center for Cancer Biology, VIB, Leuven, Belgium.
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium.
| |
Collapse
|
2
|
Dennison R, Usuga E, Chen H, Paul JZ, Arbelaez CA, Teng YD. Direct Cell Reprogramming and Phenotypic Conversion: An Analysis of Experimental Attempts to Transform Astrocytes into Neurons in Adult Animals. Cells 2023; 12:618. [PMID: 36831283 PMCID: PMC9954435 DOI: 10.3390/cells12040618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 02/17/2023] Open
Abstract
Central nervous system (CNS) repair after injury or disease remains an unresolved problem in neurobiology research and an unmet medical need. Directly reprogramming or converting astrocytes to neurons (AtN) in adult animals has been investigated as a potential strategy to facilitate brain and spinal cord recovery and advance fundamental biology. Conceptually, AtN strategies rely on forced expression or repression of lineage-specific transcription factors to make endogenous astrocytes become "induced neurons" (iNs), presumably without re-entering any pluripotent or multipotent states. The AtN-derived cells have been reported to manifest certain neuronal functions in vivo. However, this approach has raised many new questions and alternative explanations regarding the biological features of the end products (e.g., iNs versus neuron-like cells, neural functional changes, etc.), developmental biology underpinnings, and neurobiological essentials. For this paper per se, we proposed to draw an unconventional distinction between direct cell conversion and direct cell reprogramming, relative to somatic nuclear transfer, based on the experimental methods utilized to initiate the transformation process, aiming to promote a more in-depth mechanistic exploration. Moreover, we have summarized the current tactics employed for AtN induction, comparisons between the bench endeavors concerning outcome tangibility, and discussion of the issues of published AtN protocols. Lastly, the urgency to clearly define/devise the theoretical frameworks, cell biological bases, and bench specifics to experimentally validate primary data of AtN studies was highlighted.
Collapse
Affiliation(s)
- Rachel Dennison
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA 02129, USA
- Laboratory of SCI, Stem Cell and Recovery Neurobiology Research, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital Network, Mass General Brigham, and Harvard Medical School, Boston, MA 02115, USA
| | - Esteban Usuga
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA 02129, USA
- Laboratory of SCI, Stem Cell and Recovery Neurobiology Research, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital Network, Mass General Brigham, and Harvard Medical School, Boston, MA 02115, USA
| | - Harriet Chen
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA 02129, USA
- Laboratory of SCI, Stem Cell and Recovery Neurobiology Research, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital Network, Mass General Brigham, and Harvard Medical School, Boston, MA 02115, USA
| | - Jacob Z. Paul
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA 02129, USA
- Laboratory of SCI, Stem Cell and Recovery Neurobiology Research, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital Network, Mass General Brigham, and Harvard Medical School, Boston, MA 02115, USA
| | - Christian A. Arbelaez
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA 02129, USA
- Laboratory of SCI, Stem Cell and Recovery Neurobiology Research, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital Network, Mass General Brigham, and Harvard Medical School, Boston, MA 02115, USA
| | - Yang D. Teng
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA 02129, USA
- Laboratory of SCI, Stem Cell and Recovery Neurobiology Research, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital Network, Mass General Brigham, and Harvard Medical School, Boston, MA 02115, USA
- Neurotrauma Recovery Research, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital Network, Mass General Brigham, and Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
3
|
Ouyang JF, Chothani S, Rackham OJL. Deep learning models will shape the future of stem cell research. Stem Cell Reports 2023; 18:6-12. [PMID: 36630908 PMCID: PMC9860061 DOI: 10.1016/j.stemcr.2022.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/10/2022] [Accepted: 11/11/2022] [Indexed: 01/12/2023] Open
Abstract
Our ability to understand and control stem cell biology is being augmented by developments on two fronts, our ability to collect more data describing cell state and our capability to comprehend these data using deep learning models. Here we consider the impact deep learning will have in the future of stem cell research. We explore the importance of generating data suitable for these methods, the requirement for close collaboration between experimental and computational researchers, and the challenges we face to do this fairly and effectively. Achieving this will ensure that the resulting deep learning models are biologically meaningful and computationally tractable.
Collapse
Affiliation(s)
- John F Ouyang
- Duke-NUS Medical School, Program in Cardiovascular and Metabolic Disorders (CVMD) and Centre for Computational Biology (CCB), Singapore, Singapore
| | - Sonia Chothani
- Duke-NUS Medical School, Program in Cardiovascular and Metabolic Disorders (CVMD) and Centre for Computational Biology (CCB), Singapore, Singapore
| | - Owen J L Rackham
- Duke-NUS Medical School, Program in Cardiovascular and Metabolic Disorders (CVMD) and Centre for Computational Biology (CCB), Singapore, Singapore; School of Biological Sciences, University of Southampton, Southampton, UK; The Alan Turing Institute, The British Library, London, UK.
| |
Collapse
|
4
|
Hersbach BA, Fischer DS, Masserdotti G, Deeksha, Mojžišová K, Waltzhöni T, Rodriguez‐Terrones D, Heinig M, Theis FJ, Götz M, Stricker SH. Probing cell identity hierarchies by fate titration and collision during direct reprogramming. Mol Syst Biol 2022; 18:e11129. [PMID: 36106915 PMCID: PMC9476893 DOI: 10.15252/msb.202211129] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/01/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022] Open
Abstract
Despite the therapeutic promise of direct reprogramming, basic principles concerning fate erasure and the mechanisms to resolve cell identity conflicts remain unclear. To tackle these fundamental questions, we established a single-cell protocol for the simultaneous analysis of multiple cell fate conversion events based on combinatorial and traceable reprogramming factor expression: Collide-seq. Collide-seq revealed the lack of a common mechanism through which fibroblast-specific gene expression loss is initiated. Moreover, we found that the transcriptome of converting cells abruptly changes when a critical level of each reprogramming factor is attained, with higher or lower levels not contributing to major changes. By simultaneously inducing multiple competing reprogramming factors, we also found a deterministic system, in which titration of fates against each other yields dominant or colliding fates. By investigating one collision in detail, we show that reprogramming factors can disturb cell identity programs independent of their ability to bind their target genes. Taken together, Collide-seq has shed light on several fundamental principles of fate conversion that may aid in improving current reprogramming paradigms.
Collapse
Affiliation(s)
- Bob A Hersbach
- Institute of Stem Cell Research, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
- Division of Physiological Genomics, Biomedical Center MunichLudwig‐Maximilians UniversityMunichGermany
- Graduate School of Systemic Neurosciences, BiocenterLudwig‐Maximilians UniversityMunichGermany
| | - David S Fischer
- Institute of Computational Biology, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
- TUM School of Life Sciences WeihenstephanTechnical University of MunichFreisingGermany
- Department of InformaticsTechnical University of MunichMunichGermany
| | - Giacomo Masserdotti
- Institute of Stem Cell Research, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
- Division of Physiological Genomics, Biomedical Center MunichLudwig‐Maximilians UniversityMunichGermany
| | - Deeksha
- Institute of Stem Cell Research, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
- Division of Physiological Genomics, Biomedical Center MunichLudwig‐Maximilians UniversityMunichGermany
| | - Karolina Mojžišová
- Institute of Computational Biology, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
| | - Thomas Waltzhöni
- Institute of Computational Biology, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
- Core Facility GenomicsHelmholtz Zentrum MünchenOberschleißheimGermany
| | - Diego Rodriguez‐Terrones
- Institute of Computational Biology, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
- Present address:
Research Institute of Molecular Pathology (IMP)ViennaAustria
| | - Matthias Heinig
- Institute of Computational Biology, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
- Department of InformaticsTechnical University of MunichMunichGermany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
- TUM School of Life Sciences WeihenstephanTechnical University of MunichFreisingGermany
- Department of InformaticsTechnical University of MunichMunichGermany
- German Excellence Cluster of Systems NeurologyBiomedical Center MunichMunichGermany
| | - Magdalena Götz
- Institute of Stem Cell Research, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
- Division of Physiological Genomics, Biomedical Center MunichLudwig‐Maximilians UniversityMunichGermany
- German Excellence Cluster of Systems NeurologyBiomedical Center MunichMunichGermany
| | - Stefan H Stricker
- Institute of Stem Cell Research, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthOberschleißheimGermany
- Division of Physiological Genomics, Biomedical Center MunichLudwig‐Maximilians UniversityMunichGermany
| |
Collapse
|
5
|
Li B, Hon GC. Single-Cell Genomics: Catalyst for Cell Fate Engineering. Front Bioeng Biotechnol 2021; 9:748942. [PMID: 34733831 PMCID: PMC8558416 DOI: 10.3389/fbioe.2021.748942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/05/2021] [Indexed: 12/14/2022] Open
Abstract
As we near a complete catalog of mammalian cell types, the capability to engineer specific cell types on demand would transform biomedical research and regenerative medicine. However, the current pace of discovering new cell types far outstrips our ability to engineer them. One attractive strategy for cellular engineering is direct reprogramming, where induction of specific transcription factor (TF) cocktails orchestrates cell state transitions. Here, we review the foundational studies of TF-mediated reprogramming in the context of a general framework for cell fate engineering, which consists of: discovering new reprogramming cocktails, assessing engineered cells, and revealing molecular mechanisms. Traditional bulk reprogramming methods established a strong foundation for TF-mediated reprogramming, but were limited by their small scale and difficulty resolving cellular heterogeneity. Recently, single-cell technologies have overcome these challenges to rapidly accelerate progress in cell fate engineering. In the next decade, we anticipate that these tools will enable unprecedented control of cell state.
Collapse
Affiliation(s)
- Boxun Li
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Gary C. Hon
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
- Division of Basic Reproductive Biology Research, Department of Obstetrics and Gynecology, Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, United States
| |
Collapse
|
6
|
Yeo GHT, Saksena SD, Gifford DK. Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions. Nat Commun 2021; 12:3222. [PMID: 34050150 PMCID: PMC8163769 DOI: 10.1038/s41467-021-23518-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 04/22/2021] [Indexed: 12/20/2022] Open
Abstract
Existing computational methods that use single-cell RNA-sequencing (scRNA-seq) for cell fate prediction do not model how cells evolve stochastically and in physical time, nor can they predict how differentiation trajectories are altered by proposed interventions. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from time-series scRNA-seq data. We validate PRESCIENT on an experimental lineage tracing dataset, where we show that PRESCIENT is able to predict the fate biases of progenitor cells in hematopoiesis when accounting for cell proliferation, improving upon the best-performing existing method. We demonstrate how PRESCIENT can simulate trajectories for perturbed cells, recovering the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations, and is available at https://github.com/gifford-lab/prescient .
Collapse
Affiliation(s)
- Grace Hui Ting Yeo
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sachit D Saksena
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David K Gifford
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| |
Collapse
|