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Liu F, Liu Z, Cheng W, Zhao Q, Zhang X, Zhang H, Yu M, Xu H, Gao Y, Jiang Q, Shi G, Wang L, Gu S, Wang J, Cao N, Chen Z. The PERK Branch of the Unfolded Protein Response Safeguards Protein Homeostasis and Mesendoderm Specification of Human Pluripotent Stem Cells. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2303799. [PMID: 37890465 PMCID: PMC10724406 DOI: 10.1002/advs.202303799] [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: 06/12/2023] [Revised: 08/14/2023] [Indexed: 10/29/2023]
Abstract
Cardiac development involves large-scale rearrangements of the proteome. How the developing cardiac cells maintain the integrity of the proteome during the rapid lineage transition remains unclear. Here it is shown that proteotoxic stress visualized by the misfolded and/or aggregated proteins appears during early cardiac differentiation of human pluripotent stem cells and is resolved by activation of the PERK branch of unfolded protein response (UPR). PERK depletion increases misfolded and/or aggregated protein accumulation, leading to pluripotency exit defect and impaired mesendoderm specification of human pluripotent stem cells. Mechanistically, it is found that PERK safeguards mesendoderm specification through its conserved downstream effector ATF4, which subsequently activates a novel transcriptional target WARS1, to cope with the differentiation-induced proteotoxic stress. The results indicate that protein quality control represents a previously unrecognized core component of the cardiogenic regulatory network. Broadly, these findings provide a framework for understanding how UPR is integrated into the developmental program by activating the PERK-ATF4-WARS1 axis.
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Affiliation(s)
- Fang Liu
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
- Department of Clinical LaboratoryThe First Affiliated Hospital of Anhui Medical UniversityHefei230022P. R. China
| | - Zhun Liu
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
| | - Weisheng Cheng
- Prenatal Diagnosis CenterDepartment of Obstetrics and GynecologyThe First Affiliated Hospital of Anhui Medical UniversityHefei230022P. R. China
- Department of Medical InformaticsZhongshan School of MedicineSun Yat‐Sen UniversityGuangzhou510080P. R. China
| | - Qingquan Zhao
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
| | - Xinyu Zhang
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
| | - He Zhang
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
| | - Miao Yu
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
| | - He Xu
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
| | - Yichen Gao
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
| | - Qianrui Jiang
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
| | - Guojun Shi
- Guangzhou Municipal Key Laboratory of Mechanistic and Translational Obesity ResearchGuangdong Provincial Key Laboratory of DiabetologyThe Third Affiliated Hospital of Sun Yat‐Sen UniversityGuangdong510080P. R. China
| | - Likun Wang
- National Laboratory of BiomacromoleculesCAS Center for Excellence in BiomacromoleculesInstitute of BiophysicsChinese Academy of SciencesBeijing100101P. R. China
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijing100049P. R. China
| | - Shanshan Gu
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
| | - Jia Wang
- School of Health and Life SciencesUniversity of Health and Rehabilitation SciencesShandong266071China
| | - Nan Cao
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
| | - Zhongyan Chen
- Advanced Medical Technology CenterZhongshan School of Medicine and the First Affiliated HospitalSun Yat‐Sen UniversityGuangzhou510080P. R. China
- Key Laboratory for Stem Cells and Tissue EngineeringSun Yat‐Sen UniversityMinistry of EducationGuangzhou510080P. R. China
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Kurup JT, Kim S, Kidder BL. Identifying Cancer Type-Specific Transcriptional Programs through Network Analysis. Cancers (Basel) 2023; 15:4167. [PMID: 37627195 PMCID: PMC10453000 DOI: 10.3390/cancers15164167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/11/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Identifying cancer type-specific genes that define cell states is important to develop effective therapies for patients and methods for detection, early diagnosis, and prevention. While molecular mechanisms that drive malignancy have been identified for various cancers, the identification of cell-type defining transcription factors (TFs) that distinguish normal cells from cancer cells has not been fully elucidated. Here, we utilized a network biology framework, which assesses the fidelity of cell fate conversions, to identify cancer type-specific gene regulatory networks (GRN) for 17 types of cancer. Through an integrative analysis of a compendium of expression data, we elucidated core TFs and GRNs for multiple cancer types. Moreover, by comparing normal tissues and cells to cancer type-specific GRNs, we found that the expression of key network-influencing TFs can be utilized as a survival prognostic indicator for a diverse cohort of cancer patients. These findings offer a valuable resource for exploring cancer type-specific networks across a broad range of cancer types.
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Affiliation(s)
- Jiji T. Kurup
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI 48201, USA; (J.T.K.); (S.K.)
- Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Seongho Kim
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI 48201, USA; (J.T.K.); (S.K.)
- Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Benjamin L. Kidder
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI 48201, USA; (J.T.K.); (S.K.)
- Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI 48201, USA
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Lo EKW, Velazquez JJ, Peng D, Kwon C, Ebrahimkhani MR, Cahan P. Platform-agnostic CellNet enables cross-study analysis of cell fate engineering protocols. Stem Cell Reports 2023; 18:1721-1742. [PMID: 37478860 PMCID: PMC10444577 DOI: 10.1016/j.stemcr.2023.06.008] [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: 09/07/2022] [Revised: 06/16/2023] [Accepted: 06/17/2023] [Indexed: 07/23/2023] Open
Abstract
Optimization of cell engineering protocols requires standard, comprehensive quality metrics. We previously developed CellNet, a computational tool to quantitatively assess the transcriptional fidelity of engineered cells compared with their natural counterparts, based on bulk-derived expression profiles. However, this platform and others were limited in their ability to compare data from different sources, and no current tool makes it easy to compare new protocols with existing state-of-the-art protocols in a standardized manner. Here, we utilized our prior application of the top-scoring pair transformation to build a computational platform, platform-agnostic CellNet (PACNet), to address both shortcomings. To demonstrate the utility of PACNet, we applied it to thousands of samples from over 100 studies that describe dozens of protocols designed to produce seven distinct cell types. We performed an in-depth examination of hepatocyte and cardiomyocyte protocols to identify the best-performing methods, characterize the extent of intra-protocol and inter-lab variation, and identify common off-target signatures, including a surprising neural/neuroendocrine signature in primary liver-derived organoids. We have made PACNet available as an easy-to-use web application, allowing users to assess their protocols relative to our database of reference engineered samples, and as open-source, extensible code.
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Affiliation(s)
- Emily K W Lo
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Institute for Cell Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Jeremy J Velazquez
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Da Peng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Institute for Cell Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Chulan Kwon
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Institute for Cell Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Department of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Mo R Ebrahimkhani
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Institute for Cell Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
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Nell P, Kattler K, Feuerborn D, Hellwig B, Rieck A, Salhab A, Lepikhov K, Gasparoni G, Thomitzek A, Belgasmi K, Blüthgen N, Morkel M, Küppers-Munther B, Godoy P, Hay DC, Cadenas C, Marchan R, Vartak N, Edlund K, Rahnenführer J, Walter J, Hengstler JG. Identification of an FXR-modulated liver-intestine hybrid state in iPSC-derived hepatocyte-like cells. J Hepatol 2022; 77:1386-1398. [PMID: 35863491 DOI: 10.1016/j.jhep.2022.07.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 12/04/2022]
Abstract
BACKGROUND & AIMS Pluripotent stem cell (PSC)-derived hepatocyte-like cells (HLC) have enormous potential as a replacement for primary hepatocytes in drug screening, toxicology and cell replacement therapy, but their genome-wide expression patterns differ strongly from primary human hepatocytes (PHH). METHODS We differentiated human induced pluripotent stem cells (hiPSC) via definitive endoderm to HLC and characterized the cells by single-cell and bulk RNA-seq, with complementary epigenetic analyses. We then compared HLC to PHH and publicly available data on human fetal hepatocytes (FH) ex vivo; we performed bioinformatics-guided interventions to improve HLC differentiation via lentiviral transduction of the nuclear receptor FXR and agonist exposure. RESULTS Single-cell RNA-seq revealed that transcriptomes of individual HLC display a hybrid state, where hepatocyte-associated genes are expressed in concert with genes that are not expressed in PHH - mostly intestinal genes - within the same cell. Bulk-level overrepresentation analysis, as well as regulon analysis at the single-cell level, identified sets of regulatory factors discriminating HLC, FH, and PHH, hinting at a central role for the nuclear receptor FXR in the functional maturation of HLC. Combined FXR expression plus agonist exposure enhanced the expression of hepatocyte-associated genes and increased the ability of bile canalicular secretion as well as lipid droplet formation, thereby increasing HLCs' similarity to PHH. The undesired non-liver gene expression was reproducibly decreased, although only by a moderate degree. CONCLUSION In contrast to physiological hepatocyte precursor cells and mature hepatocytes, HLC co-express liver and hybrid genes in the same cell. Targeted modification of the FXR gene regulatory network improves their differentiation by suppressing intestinal traits whilst inducing hepatocyte features. LAY SUMMARY Generation of human hepatocytes from stem cells represents an active research field but its success is hampered by the fact that the stem cell-derived 'hepatocytes' still show major differences to hepatocytes obtained from a liver. Here, we identified an important reason for the difference, specifically that the stem cell-derived 'hepatocyte' represents a hybrid cell with features of hepatocytes and intestinal cells. We show that a specific protein (FXR) suppresses intestinal and induces liver features, thus bringing the stem cell-derived cells closer to hepatocytes derived from human livers.
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Affiliation(s)
- Patrick Nell
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany
| | - Kathrin Kattler
- Department of Genetics, University of Saarland, 66123 Saarbrücken, Germany
| | - David Feuerborn
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany
| | - Birte Hellwig
- Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany
| | - Adrian Rieck
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany
| | - Abdulrahman Salhab
- Department of Genetics, University of Saarland, 66123 Saarbrücken, Germany
| | | | - Gilles Gasparoni
- Department of Genetics, University of Saarland, 66123 Saarbrücken, Germany
| | - Antonia Thomitzek
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany
| | - Katharina Belgasmi
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany
| | - Nils Blüthgen
- IRI Life Sciences, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Markus Morkel
- Institute of Pathology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Barbara Küppers-Munther
- Takara Bio Europe AB (former Cellartis AB), Arvid Wallgrens Backe 20, 41346 Gothenburg, Sweden
| | - Patricio Godoy
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany
| | - David C Hay
- Institute of Regeneration and Repair, Centre for Regenerative Medicine, University of Edinburgh, Edinburgh EH16 4UU, United Kingdom
| | - Cristina Cadenas
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany
| | - Rosemarie Marchan
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany
| | - Nachiket Vartak
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany
| | - Karolina Edlund
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany
| | - Jörg Rahnenführer
- Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany
| | - Jörn Walter
- Department of Genetics, University of Saarland, 66123 Saarbrücken, Germany
| | - Jan G Hengstler
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, 44139 Dortmund, Germany.
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Ouyang Z, Bourgeois-Tchir N, Lyashenko E, Cundiff PE, Cullen PF, Challa R, Li K, Zhang X, Casey F, Engle SJ, Zhang B, Zavodszky MI. Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap. Sci Rep 2022; 12:17394. [PMID: 36253414 PMCID: PMC9576729 DOI: 10.1038/s41598-022-22115-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 10/10/2022] [Indexed: 01/10/2023] Open
Abstract
Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.
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Affiliation(s)
- Zhengyu Ouyang
- BioInfoRx, Inc., 510 Charmany Dr, Suite 275A, Madison, WI 53719 USA
| | - Nathanael Bourgeois-Tchir
- grid.417832.b0000 0004 0384 8146Translational Biology, Research and Development, Biogen, Inc., 225 Binney St, Cambridge, MA 02142 USA
| | - Eugenia Lyashenko
- grid.417832.b0000 0004 0384 8146Translational Biology, Research and Development, Biogen, Inc., 225 Binney St, Cambridge, MA 02142 USA ,grid.417555.70000 0000 8814 392XPresent Address: Genomic Medicine Unit, Sanofi, 225 2nd Ave, Waltham, MA 02451 USA
| | - Paige E. Cundiff
- grid.417832.b0000 0004 0384 8146Translational Biology, Research and Development, Biogen, Inc., 225 Binney St, Cambridge, MA 02142 USA
| | - Patrick F. Cullen
- grid.417832.b0000 0004 0384 8146Translational Biology, Research and Development, Biogen, Inc., 225 Binney St, Cambridge, MA 02142 USA
| | - Ravi Challa
- grid.417832.b0000 0004 0384 8146Translational Biology, Research and Development, Biogen, Inc., 225 Binney St, Cambridge, MA 02142 USA
| | - Kejie Li
- grid.417832.b0000 0004 0384 8146Translational Biology, Research and Development, Biogen, Inc., 225 Binney St, Cambridge, MA 02142 USA
| | - Xinmin Zhang
- BioInfoRx, Inc., 510 Charmany Dr, Suite 275A, Madison, WI 53719 USA
| | - Fergal Casey
- grid.417832.b0000 0004 0384 8146Translational Biology, Research and Development, Biogen, Inc., 225 Binney St, Cambridge, MA 02142 USA
| | - Sandra J. Engle
- grid.417832.b0000 0004 0384 8146Translational Biology, Research and Development, Biogen, Inc., 225 Binney St, Cambridge, MA 02142 USA
| | - Baohong Zhang
- grid.417832.b0000 0004 0384 8146Translational Biology, Research and Development, Biogen, Inc., 225 Binney St, Cambridge, MA 02142 USA
| | - Maria I. Zavodszky
- grid.417832.b0000 0004 0384 8146Translational Biology, Research and Development, Biogen, Inc., 225 Binney St, Cambridge, MA 02142 USA
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A comprehensive transcriptomic comparison of hepatocyte model systems improves selection of models for experimental use. Commun Biol 2022; 5:1094. [PMID: 36241695 PMCID: PMC9568534 DOI: 10.1038/s42003-022-04046-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/28/2022] [Indexed: 11/08/2022] Open
Abstract
The myriad of available hepatocyte in vitro models provides researchers the possibility to select hepatocyte-like cells (HLCs) for specific research goals. However, direct comparison of hepatocyte models is currently challenging. We systematically searched the literature and compared different HLCs, but reported functions were limited to a small subset of hepatic functions. To enable a more comprehensive comparison, we developed an algorithm to compare transcriptomic data across studies that tested HLCs derived from hepatocytes, biliary cells, fibroblasts, and pluripotent stem cells, alongside primary human hepatocytes (PHHs). This revealed that no HLC covered the complete hepatic transcriptome, highlighting the importance of HLC selection. HLCs derived from hepatocytes had the highest transcriptional resemblance to PHHs regardless of the protocol, whereas the quality of fibroblasts and PSC derived HLCs varied depending on the protocol used. Finally, we developed and validated a web application (HLCompR) enabling comparison for specific pathways and addition of new HLCs. In conclusion, our comprehensive transcriptomic comparison of HLCs allows selection of HLCs for specific research questions and can guide improvements in culturing conditions.
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7
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Transcriptomics analysis of human iPSC-derived dopaminergic neurons reveals a novel model for sporadic Parkinson's disease. Mol Psychiatry 2022; 27:4355-4367. [PMID: 35725899 DOI: 10.1038/s41380-022-01663-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/31/2022] [Accepted: 06/07/2022] [Indexed: 02/07/2023]
Abstract
Parkinson's disease (PD) is a progressive, neurodegenerative disease affecting over 1% of the population beyond 65 years of age. Although some PD cases are inheritable, the majority of PD cases occur in a sporadic manner. Risk factors comprise next to heredity, age, and gender also exposure to neurotoxins from for instance pesticides and herbicides. As PD is characterized by a loss of dopaminergic neurons in the substantia nigra, it is nearly impossible to access and extract these cells from patients for investigating disease mechanisms. The emergence of induced pluripotent stem (iPSC) technology allows differentiating and growing human dopaminergic neurons, which can be used for in vitro disease modeling. Here, we differentiated human iPSCs into dopaminergic neurons, and subsequently exposed the cells to increasing concentrations of the neurotoxin MPP+. Temporal transcriptomics analysis revealed a strong time- and dose-dependent response with genes over-represented across pathways involved in PD etiology such as "Parkinson's Disease", "Dopaminergic signaling" and "calcium signaling". Moreover, we validated this disease model by showing robust overlap with a meta-analysis of transcriptomics data from substantia nigra from post-mortem PD patients. The overlap included genes linked to e.g. mitochondrial dysfunction, neuron differentiation, apoptosis and inflammation. Our data shows, that MPP+-induced, human iPSC-derived dopaminergic neurons present molecular perturbations as observed in the etiology of PD. Therefore we propose iPSC-derived dopaminergic neurons as a foundation for a novel sporadic PD model to study the pathomolecular mechanisms of PD, but also to screen for novel anti-PD drugs and to develop and test new treatment strategies.
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8
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Jing R, Scarfo I, Najia MA, Lummertz da Rocha E, Han A, Sanborn M, Bingham T, Kubaczka C, Jha DK, Falchetti M, Schlaeger TM, North TE, Maus MV, Daley GQ. EZH1 repression generates mature iPSC-derived CAR T cells with enhanced antitumor activity. Cell Stem Cell 2022; 29:1181-1196.e6. [PMID: 35931029 PMCID: PMC9386785 DOI: 10.1016/j.stem.2022.06.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/31/2022] [Accepted: 06/29/2022] [Indexed: 01/12/2023]
Abstract
Human induced pluripotent stem cells (iPSCs) provide a potentially unlimited resource for cell therapies, but the derivation of mature cell types remains challenging. The histone methyltransferase EZH1 is a negative regulator of lymphoid potential during embryonic hematopoiesis. Here, we demonstrate that EZH1 repression facilitates in vitro differentiation and maturation of T cells from iPSCs. Coupling a stroma-free T cell differentiation system with EZH1-knockdown-mediated epigenetic reprogramming, we generated iPSC-derived T cells, termed EZ-T cells, which display a highly diverse T cell receptor (TCR) repertoire and mature molecular signatures similar to those of TCRαβ T cells from peripheral blood. Upon activation, EZ-T cells give rise to effector and memory T cell subsets. When transduced with chimeric antigen receptors (CARs), EZ-T cells exhibit potent antitumor activities in vitro and in xenograft models. Epigenetic remodeling via EZH1 repression allows efficient production of developmentally mature T cells from iPSCs for applications in adoptive cell therapy.
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Affiliation(s)
- Ran Jing
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Irene Scarfo
- Cellular Immunotherapy Program, Massachusetts General Hospital Cancer Center, Charlestown, MA 02114, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Mohamad Ali Najia
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Edroaldo Lummertz da Rocha
- Department of Microbiology, Immunology and Parasitology, Federal University of Santa Catarina, Florianópolis, SC 88040-900, Brazil
| | - Areum Han
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Michael Sanborn
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Trevor Bingham
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Caroline Kubaczka
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Deepak K Jha
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Marcelo Falchetti
- Graduate Program of Pharmacology, Center for Biological Sciences, Federal University of Santa Catarina, Florianópolis, SC 88040-900, Brazil
| | - Thorsten M Schlaeger
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Boston, MA 02115, USA
| | - Trista E North
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Boston, MA 02115, USA; Developmental and Regenerative Biology Program, Harvard Medical School, Boston, MA 02115, USA
| | - Marcela V Maus
- Cellular Immunotherapy Program, Massachusetts General Hospital Cancer Center, Charlestown, MA 02114, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - George Q Daley
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Boston, MA 02115, USA; Developmental and Regenerative Biology Program, Harvard Medical School, Boston, MA 02115, USA.
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9
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Hammelman J, Patel T, Closser M, Wichterle H, Gifford D. Ranking reprogramming factors for cell differentiation. Nat Methods 2022; 19:812-822. [PMID: 35710610 PMCID: PMC10460539 DOI: 10.1038/s41592-022-01522-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 05/13/2022] [Indexed: 12/16/2022]
Abstract
Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. Here we examine the success rate of methods and data for differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF and DeepAccess) to discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that use gene expression, biological networks and chromatin accessibility data, and comprehensively test parameter and preprocessing of input data to optimize performance. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top ten candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF have higher correlation with the ranked significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and diffTF are optimal methods for transcription factor recovery that will allow for systematic prioritization of transcription factor candidates to aid in the design of new reprogramming protocols.
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Affiliation(s)
- Jennifer Hammelman
- Computational and Systems Biology, MIT, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
| | - Tulsi Patel
- Departments of Pathology and Cell Biology, Neuroscience, Rehabilitation and Regenerative Medicine (in Neurology), Columbia University Irving Medical Center, New York, NY, USA
- Center for Motor Neuron Biology and Disease, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY, USA
| | - Michael Closser
- Departments of Pathology and Cell Biology, Neuroscience, Rehabilitation and Regenerative Medicine (in Neurology), Columbia University Irving Medical Center, New York, NY, USA
- Center for Motor Neuron Biology and Disease, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY, USA
| | - Hynek Wichterle
- Departments of Pathology and Cell Biology, Neuroscience, Rehabilitation and Regenerative Medicine (in Neurology), Columbia University Irving Medical Center, New York, NY, USA
- Center for Motor Neuron Biology and Disease, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY, USA
| | - David Gifford
- Computational and Systems Biology, MIT, Cambridge, MA, USA.
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
- Department of Biological Engineering, MIT, Cambridge, MA, USA.
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA.
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10
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Basu B, Gowtham N, Xiao Y, Kalidindi SR, Leong KW. Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials. Acta Biomater 2022; 143:1-25. [PMID: 35202854 DOI: 10.1016/j.actbio.2022.02.027] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 12/12/2022]
Abstract
Conventional approaches to developing biomaterials and implants require intuitive tailoring of manufacturing protocols and biocompatibility assessment. This leads to longer development cycles, and high costs. To meet existing and unmet clinical needs, it is critical to accelerate the production of implantable biomaterials, implants and biomedical devices. Building on the Materials Genome Initiative, we define the concept 'biomaterialomics' as the integration of multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools throughout the entire pipeline of biomaterials development. The Data Science-driven approach is envisioned to bring together on a single platform, the computational tools, databases, experimental methods, machine learning, and advanced manufacturing (e.g., 3D printing) to develop the fourth-generation biomaterials and implants, whose clinical performance will be predicted using 'digital twins'. While analysing the key elements of the concept of 'biomaterialomics', significant emphasis has been put forward to effectively utilize high-throughput biocompatibility data together with multiscale physics-based models, E-platform/online databases of clinical studies, data science approaches, including metadata management, AI/ Machine Learning (ML) algorithms and uncertainty predictions. Such integrated formulation will allow one to adopt cross-disciplinary approaches to establish processing-structure-property (PSP) linkages. A few published studies from the lead author's research group serve as representative examples to illustrate the formulation and relevance of the 'Biomaterialomics' approaches for three emerging research themes, i.e. patient-specific implants, additive manufacturing, and bioelectronic medicine. The increased adaptability of AI/ML tools in biomaterials science along with the training of the next generation researchers in data science are strongly recommended. STATEMENT OF SIGNIFICANCE: This leading opinion review paper emphasizes the need to integrate the concepts and algorithms of the data science with biomaterials science. Also, this paper emphasizes the need to establish a mathematically rigorous cross-disciplinary framework that will allow a systematic quantitative exploration and curation of critical biomaterials knowledge needed to drive objectively the innovation efforts within a suitable uncertainty quantification framework, as embodied in 'biomaterialomics' concept, which integrates multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools, like machine learning. The formulation of this approach has been demonstrated for patient-specific implants, additive manufacturing, and bioelectronic medicine.
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11
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Mellis IA, Edelstein HI, Truitt R, Goyal Y, Beck LE, Symmons O, Dunagin MC, Linares Saldana RA, Shah PP, Pérez-Bermejo JA, Padmanabhan A, Yang W, Jain R, Raj A. Responsiveness to perturbations is a hallmark of transcription factors that maintain cell identity in vitro. Cell Syst 2021; 12:885-899.e8. [PMID: 34352221 PMCID: PMC8522198 DOI: 10.1016/j.cels.2021.07.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/27/2020] [Accepted: 07/09/2021] [Indexed: 02/07/2023]
Abstract
Identifying the particular transcription factors that maintain cell type in vitro is important for manipulating cell type. Identifying such transcription factors by their cell-type-specific expression or their involvement in developmental regulation has had limited success. We hypothesized that because cell type is often resilient to perturbations, the transcriptional response to perturbations would reveal identity-maintaining transcription factors. We developed perturbation panel profiling (P3) as a framework for perturbing cells across many conditions and measuring gene expression responsiveness transcriptome-wide. In human iPSC-derived cardiac myocytes, P3 showed that transcription factors important for cardiac myocyte differentiation and maintenance were among the most frequently upregulated (most responsive). We reasoned that one function of responsive genes may be to maintain cellular identity. We identified responsive transcription factors in fibroblasts using P3 and found that suppressing their expression led to enhanced reprogramming. We propose that responsiveness to perturbations is a property of transcription factors that help maintain cellular identity in vitro. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Ian A Mellis
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Genomics and Computational Biology Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hailey I Edelstein
- Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rachel Truitt
- Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogesh Goyal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren E Beck
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Orsolya Symmons
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Margaret C Dunagin
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Ricardo A Linares Saldana
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Parisha P Shah
- Department of Medicine, 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
| | | | - Arun Padmanabhan
- Gladstone Institute of Cardiovascular Disease, San Francisco, CA, USA; Division of Cardiology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Wenli Yang
- Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rajan Jain
- Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, 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; Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Arjun Raj
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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12
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Gupta R, Kleinjans J, Caiment F. Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning. BMC Cancer 2021; 21:962. [PMID: 34445986 PMCID: PMC8394105 DOI: 10.1186/s12885-021-08704-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 08/09/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, both have limited efficiency and are ineffective in early prognosis. Due to such limitations, we propose to use RNA-Seq data for evaluating putative higher accuracy biomarkers at the transcript level that could help in early prognosis. METHODS To identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning algorithms: random forest, K-nearest neighbor, Naïve Bayes, support vector machine, and neural networks. Various metrics, namely sensitivity, specificity, MCC, informedness, and AUC-ROC (except for support vector machine) were evaluated. The algorithms that produced the highest values for all metrics were chosen to extract the top features that were subjected to recursive feature elimination. Through recursive feature elimination, the least number of features were obtained to differentiate between the healthy and HCC cell models. RESULTS From the metrics used, it is demonstrated that the efficiency of the known protein biomarkers for HCC is comparatively lower than complete transcriptomics data. Among the different machine learning algorithms, random forest and support vector machine demonstrated the best performance. Using recursive feature elimination on top features of random forest and support vector machine three transcripts were selected that had an accuracy of 0.97 and kappa of 0.93. Of the three transcripts, two were protein coding (PARP2-202 and SPON2-203) and one was a non-coding transcript (CYREN-211). Lastly, we demonstrated that these three selected transcripts outperformed randomly taken three transcripts (15,000 combinations), hence were not chance findings, and could then be an interesting candidate for new HCC biomarker development. CONCLUSION Using RNA-Seq data combined with machine learning approaches can aid in finding novel transcript biomarkers. The three biomarkers identified: PARP2-202, SPON2-203, and CYREN-211, presented the highest accuracy among all other transcripts in differentiating the healthy and HCC cell models. The machine learning pipeline developed in this study can be used for any RNA-Seq dataset to find novel transcript biomarkers. Code: www.github.com/rajinder4489/ML_biomarkers.
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Affiliation(s)
- Rajinder Gupta
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
| | - Jos Kleinjans
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
| | - Florian Caiment
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands.
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13
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Identification of pathological transcription in autosomal dominant polycystic kidney disease epithelia. Sci Rep 2021; 11:15139. [PMID: 34301992 PMCID: PMC8302622 DOI: 10.1038/s41598-021-94442-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 07/08/2021] [Indexed: 11/09/2022] Open
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) affects more than 12 million people worldwide. Mutations in PKD1 and PKD2 cause cyst formation through unknown mechanisms. To unravel the pathogenic mechanisms in ADPKD, multiple studies have investigated transcriptional mis-regulation in cystic kidneys from patients and mouse models, and numerous dysregulated genes and pathways have been described. Yet, the concordance between studies has been rather limited. Furthermore, the cellular and genetic diversity in cystic kidneys has hampered the identification of mis-expressed genes in kidney epithelial cells with homozygous PKD mutations, which are critical to identify polycystin-dependent pathways. Here we performed transcriptomic analyses of Pkd1- and Pkd2-deficient mIMCD3 kidney epithelial cells followed by a meta-analysis to integrate all published ADPKD transcriptomic data sets. Based on the hypothesis that Pkd1 and Pkd2 operate in a common pathway, we first determined transcripts that are differentially regulated by both genes. RNA sequencing of genome-edited ADPKD kidney epithelial cells identified 178 genes that are concordantly regulated by Pkd1 and Pkd2. Subsequent integration of existing transcriptomic studies confirmed 31 previously described genes and identified 61 novel genes regulated by Pkd1 and Pkd2. Cluster analyses then linked Pkd1 and Pkd2 to mRNA splicing, specific factors of epithelial mesenchymal transition, post-translational protein modification and epithelial cell differentiation, including CD34, CDH2, CSF2RA, DLX5, HOXC9, PIK3R1, PLCB1 and TLR6. Taken together, this model-based integrative analysis of transcriptomic alterations in ADPKD annotated a conserved core transcriptomic profile and identified novel candidate genes for further experimental studies.
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14
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Chandrasekaran V, Carta G, da Costa Pereira D, Gupta R, Murphy C, Feifel E, Kern G, Lechner J, Cavallo AL, Gupta S, Caiment F, Kleinjans JCS, Gstraunthaler G, Jennings P, Wilmes A. Generation and characterization of iPSC-derived renal proximal tubule-like cells with extended stability. Sci Rep 2021; 11:11575. [PMID: 34078926 PMCID: PMC8172841 DOI: 10.1038/s41598-021-89550-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 04/23/2021] [Indexed: 12/21/2022] Open
Abstract
The renal proximal tubule is responsible for re-absorption of the majority of the glomerular filtrate and its proper function is necessary for whole-body homeostasis. Aging, certain diseases and chemical-induced toxicity are factors that contribute to proximal tubule injury and chronic kidney disease progression. To better understand these processes, it would be advantageous to generate renal tissues from human induced pluripotent stem cells (iPSC). Here, we report the differentiation and characterization of iPSC lines into proximal tubular-like cells (PTL). The protocol is a step wise exposure of small molecules and growth factors, including the GSK3 inhibitor (CHIR99021), the retinoic acid receptor activator (TTNPB), FGF9 and EGF, to drive iPSC to PTL via cell stages representing characteristics of early stages of renal development. Genome-wide RNA sequencing showed that PTL clustered within a kidney phenotype. PTL expressed proximal tubular-specific markers, including megalin (LRP2), showed a polarized phenotype, and were responsive to parathyroid hormone. PTL could take up albumin and exhibited ABCB1 transport activity. The phenotype was stable for up to 7 days and was maintained after passaging. This protocol will form the basis of an optimized strategy for molecular investigations using iPSC derived PTL.
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Affiliation(s)
- Vidya Chandrasekaran
- Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081, HZ, Amsterdam, The Netherlands
| | - Giada Carta
- Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081, HZ, Amsterdam, The Netherlands
| | - Daniel da Costa Pereira
- Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081, HZ, Amsterdam, The Netherlands
| | - Rajinder Gupta
- Department of Toxicogenomics, Maastricht University, School of Oncology and Developmental Biology (GROW), Maastricht, The Netherlands
| | - Cormac Murphy
- Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081, HZ, Amsterdam, The Netherlands
| | - Elisabeth Feifel
- Institute of Physiology and Medical Physics, Medical University of Innsbruck, Innsbruck, Austria
| | - Georg Kern
- Institute of Physiology and Medical Physics, Medical University of Innsbruck, Innsbruck, Austria
| | - Judith Lechner
- Institute of Physiology and Medical Physics, Medical University of Innsbruck, Innsbruck, Austria
| | | | | | - Florian Caiment
- Department of Toxicogenomics, Maastricht University, School of Oncology and Developmental Biology (GROW), Maastricht, The Netherlands
| | - Jos C S Kleinjans
- Department of Toxicogenomics, Maastricht University, School of Oncology and Developmental Biology (GROW), Maastricht, The Netherlands
| | - Gerhard Gstraunthaler
- Institute of Physiology and Medical Physics, Medical University of Innsbruck, Innsbruck, Austria
| | - Paul Jennings
- Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081, HZ, Amsterdam, The Netherlands.
| | - Anja Wilmes
- Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081, HZ, Amsterdam, The Netherlands.
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15
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Peng D, Gleyzer R, Tai WH, Kumar P, Bian Q, Isaacs B, da Rocha EL, Cai S, DiNapoli K, Huang FW, Cahan P. Evaluating the transcriptional fidelity of cancer models. Genome Med 2021; 13:73. [PMID: 33926541 PMCID: PMC8086312 DOI: 10.1186/s13073-021-00888-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 04/15/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Cancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under investigation; however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors. METHODS We developed a machine learning-based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient-derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence. RESULTS We have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types. However, several patient-derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity. CONCLUSIONS CancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as a freely downloadable R package ( https://github.com/pcahan1/cancerCellNet ) and as a web application ( http://www.cahanlab.org/resources/cancerCellNet_web ) that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.
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Affiliation(s)
- Da Peng
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Rachel Gleyzer
- grid.21107.350000 0001 2171 9311Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Wen-Hsin Tai
- grid.21107.350000 0001 2171 9311Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Pavithra Kumar
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA ,grid.21107.350000 0001 2171 9311Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Qin Bian
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA ,grid.21107.350000 0001 2171 9311Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Bradley Isaacs
- grid.21107.350000 0001 2171 9311Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Edroaldo Lummertz da Rocha
- grid.411237.20000 0001 2188 7235Department of Microbiology, Immunology and Parasitology, Federal University of Santa Catarina, Florianópolis, SC Brazil
| | - Stephanie Cai
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Kathleen DiNapoli
- grid.21107.350000 0001 2171 9311Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA ,grid.21107.350000 0001 2171 9311Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
| | - Franklin W. Huang
- grid.266102.10000 0001 2297 6811Division of Hematology/Oncology, Department of Medicine; Helen Diller Family Cancer Center; Bakar Computational Health Sciences Institute; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA USA
| | - Patrick Cahan
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA ,grid.21107.350000 0001 2171 9311Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA ,grid.21107.350000 0001 2171 9311Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
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16
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Ng AHM, Khoshakhlagh P, Rojo Arias JE, Pasquini G, Wang K, Swiersy A, Shipman SL, Appleton E, Kiaee K, Kohman RE, Vernet A, Dysart M, Leeper K, Saylor W, Huang JY, Graveline A, Taipale J, Hill DE, Vidal M, Melero-Martin JM, Busskamp V, Church GM. A comprehensive library of human transcription factors for cell fate engineering. Nat Biotechnol 2021; 39:510-519. [PMID: 33257861 PMCID: PMC7610615 DOI: 10.1038/s41587-020-0742-6] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 10/19/2020] [Indexed: 02/06/2023]
Abstract
Human pluripotent stem cells (hPSCs) offer an unprecedented opportunity to model diverse cell types and tissues. To enable systematic exploration of the programming landscape mediated by transcription factors (TFs), we present the Human TFome, a comprehensive library containing 1,564 TF genes and 1,732 TF splice isoforms. By screening the library in three hPSC lines, we discovered 290 TFs, including 241 that were previously unreported, that induce differentiation in 4 days without alteration of external soluble or biomechanical cues. We used four of the hits to program hPSCs into neurons, fibroblasts, oligodendrocytes and vascular endothelial-like cells that have molecular and functional similarity to primary cells. Our cell-autonomous approach enabled parallel programming of hPSCs into multiple cell types simultaneously. We also demonstrated orthogonal programming by including oligodendrocyte-inducible hPSCs with unmodified hPSCs to generate cerebral organoids, which expedited in situ myelination. Large-scale combinatorial screening of the Human TFome will complement other strategies for cell engineering based on developmental biology and computational systems biology.
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Affiliation(s)
- Alex H M Ng
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
- GC Therapeutics, Inc, Cambridge, MA, USA
| | - Parastoo Khoshakhlagh
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
- GC Therapeutics, Inc, Cambridge, MA, USA
| | - Jesus Eduardo Rojo Arias
- Technische Universität Dresden, Center for Molecular and Cellular Bioengineering (CMCB), Center for Regenerative Therapies Dresden (CRTD), Dresden, Germany
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Giovanni Pasquini
- Technische Universität Dresden, Center for Molecular and Cellular Bioengineering (CMCB), Center for Regenerative Therapies Dresden (CRTD), Dresden, Germany
| | - Kai Wang
- Department of Cardiac Surgery, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
| | - Anka Swiersy
- Technische Universität Dresden, Center for Molecular and Cellular Bioengineering (CMCB), Center for Regenerative Therapies Dresden (CRTD), Dresden, Germany
| | - Seth L Shipman
- Gladstone Institutes and University of California, San Francisco, San Francisco, CA, USA
| | - Evan Appleton
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
- GC Therapeutics, Inc, Cambridge, MA, USA
| | - Kiavash Kiaee
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
- GC Therapeutics, Inc, Cambridge, MA, USA
| | - Richie E Kohman
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Andyna Vernet
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Matthew Dysart
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Kathleen Leeper
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Wren Saylor
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Jeremy Y Huang
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Amanda Graveline
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
| | - Jussi Taipale
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
- Applied Tumor Genomics Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - David E Hill
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Marc Vidal
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Juan M Melero-Martin
- Department of Cardiac Surgery, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
| | - Volker Busskamp
- Technische Universität Dresden, Center for Molecular and Cellular Bioengineering (CMCB), Center for Regenerative Therapies Dresden (CRTD), Dresden, Germany.
- Department of Ophthalmology, Medical Faculty, University of Bonn, Bonn, Germany.
| | - George M Church
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA.
- GC Therapeutics, Inc, Cambridge, MA, USA.
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17
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Papantoniou I, Nilsson Hall G, Loverdou N, Lesage R, Herpelinck T, Mendes L, Geris L. Turning Nature's own processes into design strategies for living bone implant biomanufacturing: a decade of Developmental Engineering. Adv Drug Deliv Rev 2021; 169:22-39. [PMID: 33290762 PMCID: PMC7839840 DOI: 10.1016/j.addr.2020.11.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 11/20/2020] [Accepted: 11/29/2020] [Indexed: 12/14/2022]
Abstract
A decade after the term developmental engineering (DE) was coined to indicate the use of developmental processes as blueprints for the design and development of engineered living implants, a myriad of proof-of-concept studies demonstrate the potential of this approach in small animal models. This review provides an overview of DE work, focusing on applications in bone regeneration. Enabling technologies allow to quantify the distance between in vitro processes and their developmental counterpart, as well as to design strategies to reduce that distance. By embedding Nature's robust mechanisms of action in engineered constructs, predictive large animal data and subsequent positive clinical outcomes can be gradually achieved. To this end, the development of next generation biofabrication technologies should provide the necessary scale and precision for robust living bone implant biomanufacturing.
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Affiliation(s)
- Ioannis Papantoniou
- Institute of Chemical Engineering Sciences, Foundation for Research and Technology - Hellas (FORTH), Stadiou street, 26504 Patras, Greece; Skeletal Biology & Engineering Research Center, KU Leuven, Herestraat 49 (813), 3000 Leuven, Belgium; Prometheus, The KU Leuven R&D Division for Skeletal Tissue Engineering, Herestraat 49 (813), 3000 Leuven, Belgium.
| | - Gabriella Nilsson Hall
- Skeletal Biology & Engineering Research Center, KU Leuven, Herestraat 49 (813), 3000 Leuven, Belgium; Prometheus, The KU Leuven R&D Division for Skeletal Tissue Engineering, Herestraat 49 (813), 3000 Leuven, Belgium.
| | - Niki Loverdou
- Prometheus, The KU Leuven R&D Division for Skeletal Tissue Engineering, Herestraat 49 (813), 3000 Leuven, Belgium; GIGA in silico medicine, University of Liège, Avenue de l'Hôpital 11 (B34), 4000 Liège, Belgium; Biomechanics Section, KU Leuven, Celestijnenlaan 300C (2419), 3001 Leuven, Belgium.
| | - Raphaelle Lesage
- Prometheus, The KU Leuven R&D Division for Skeletal Tissue Engineering, Herestraat 49 (813), 3000 Leuven, Belgium; Biomechanics Section, KU Leuven, Celestijnenlaan 300C (2419), 3001 Leuven, Belgium.
| | - Tim Herpelinck
- Skeletal Biology & Engineering Research Center, KU Leuven, Herestraat 49 (813), 3000 Leuven, Belgium; Prometheus, The KU Leuven R&D Division for Skeletal Tissue Engineering, Herestraat 49 (813), 3000 Leuven, Belgium.
| | - Luis Mendes
- Skeletal Biology & Engineering Research Center, KU Leuven, Herestraat 49 (813), 3000 Leuven, Belgium; Prometheus, The KU Leuven R&D Division for Skeletal Tissue Engineering, Herestraat 49 (813), 3000 Leuven, Belgium.
| | - Liesbet Geris
- Skeletal Biology & Engineering Research Center, KU Leuven, Herestraat 49 (813), 3000 Leuven, Belgium; GIGA in silico medicine, University of Liège, Avenue de l'Hôpital 11 (B34), 4000 Liège, Belgium; Prometheus, The KU Leuven R&D Division for Skeletal Tissue Engineering, Herestraat 49 (813), 3000 Leuven, Belgium; Biomechanics Section, KU Leuven, Celestijnenlaan 300C (2419), 3001 Leuven, Belgium.
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18
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Velazquez JJ, LeGraw R, Moghadam F, Tan Y, Kilbourne J, Maggiore JC, Hislop J, Liu S, Cats D, Chuva de Sousa Lopes SM, Plaisier C, Cahan P, Kiani S, Ebrahimkhani MR. Gene Regulatory Network Analysis and Engineering Directs Development and Vascularization of Multilineage Human Liver Organoids. Cell Syst 2020; 12:41-55.e11. [PMID: 33290741 PMCID: PMC8164844 DOI: 10.1016/j.cels.2020.11.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 08/13/2020] [Accepted: 11/09/2020] [Indexed: 12/19/2022]
Abstract
Pluripotent stem cell (PSC)-derived organoids have emerged as novel multicellular models of human tissue development but display immature phenotypes, aberrant tissue fates, and a limited subset of cells. Here, we demonstrate that integrated analysis and engineering of gene regulatory networks (GRNs) in PSC-derived multilineage human liver organoids direct maturation and vascular morphogenesis in vitro. Overexpression of PROX1 and ATF5, combined with targeted CRISPR-based transcriptional activation of endogenous CYP3A4, reprograms tissue GRNs and improves native liver functions, such as FXR signaling, CYP3A4 enzymatic activity, and stromal cell reactivity. The engineered tissues possess superior liver identity when compared with other PSC-derived liver organoids and show the presence of hepatocyte, biliary, endothelial, and stellate-like cell populations in single-cell RNA-seq analysis. Finally, they show hepatic functions when studied in vivo. Collectively, our approach provides an experimental framework to direct organogenesis in vitro by systematically probing molecular pathways and transcriptional networks that promote tissue development.
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Affiliation(s)
- Jeremy J Velazquez
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Ryan LeGraw
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Farzaneh Moghadam
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Yuqi Tan
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Institute for Cell Engineering Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | | | - Joseph C Maggiore
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Joshua Hislop
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Silvia Liu
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Davy Cats
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Einthovenweg, 2333 ZC Leiden, the Netherlands
| | - Susana M Chuva de Sousa Lopes
- Department of Anatomy and Embryology, Leiden University Medical Center, Einthovenweg, 2333 ZC Leiden, the Netherlands
| | - Christopher Plaisier
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Patrick Cahan
- Institute for Cell Engineering Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Samira Kiani
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Mo R Ebrahimkhani
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85281, USA; Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA; Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA; Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine and Science, Phoenix, AZ 85054, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA.
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19
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Dotson GA, Ryan CW, Chen C, Muir L, Rajapakse I. Cellular reprogramming: Mathematics meets medicine. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2020; 13:e1515. [PMID: 33289324 PMCID: PMC8867497 DOI: 10.1002/wsbm.1515] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 11/05/2020] [Accepted: 11/09/2020] [Indexed: 11/11/2022]
Abstract
Generating needed cell types using cellular reprogramming is a promising strategy for restoring tissue function in injury or disease. A common method for reprogramming is addition of one or more transcription factors that confer a new function or identity. Advancements in transcription factor selection and delivery have culminated in successful grafting of autologous reprogrammed cells, an early demonstration of their clinical utility. Though cellular reprogramming has been successful in a number of settings, identification of appropriate transcription factors for a particular transformation has been challenging. Computational methods enable more sophisticated prediction of relevant transcription factors for reprogramming by leveraging gene expression data of initial and target cell types, and are built on mathematical frameworks ranging from information theory to control theory. This review highlights the utility and impact of these mathematical frameworks in the field of cellular reprogramming. This article is categorized under: Reproductive System Diseases > Reproductive System Diseases>Genetics/Genomics/Epigenetics Reproductive System Diseases > Reproductive System Diseases>Stem Cells and Development Reproductive System Diseases > Reproductive System Diseases>Computational Models.
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Affiliation(s)
- Gabrielle A. Dotson
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Charles W. Ryan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109, USA
- Program in Cellular and Molecular Biology, University of Michigan, Ann Arbor, Michigan, 48109, USA
- Medical Scientist Training Program, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Can Chen
- Department of Mathematics, University of Michigan, Ann Arbor, Michigan, 48109, USA
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Lindsey Muir
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Indika Rajapakse
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109, USA
- Department of Mathematics, University of Michigan, Ann Arbor, Michigan, 48109, USA
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20
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Gupta R, Schrooders Y, Hauser D, van Herwijnen M, Albrecht W, Ter Braak B, Brecklinghaus T, Castell JV, Elenschneider L, Escher S, Guye P, Hengstler JG, Ghallab A, Hansen T, Leist M, Maclennan R, Moritz W, Tolosa L, Tricot T, Verfaillie C, Walker P, van de Water B, Kleinjans J, Caiment F. Comparing in vitro human liver models to in vivo human liver using RNA-Seq. Arch Toxicol 2020; 95:573-589. [PMID: 33106934 PMCID: PMC7870774 DOI: 10.1007/s00204-020-02937-6] [Citation(s) in RCA: 174] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 10/12/2020] [Indexed: 01/29/2023]
Abstract
The liver plays an important role in xenobiotic metabolism and represents a primary target for toxic substances. Many different in vitro cell models have been developed in the past decades. In this study, we used RNA-sequencing (RNA-Seq) to analyze the following human in vitro liver cell models in comparison to human liver tissue: cancer-derived cell lines (HepG2, HepaRG 3D), induced pluripotent stem cell-derived hepatocyte-like cells (iPSC-HLCs), cancerous human liver-derived assays (hPCLiS, human precision cut liver slices), non-cancerous human liver-derived assays (PHH, primary human hepatocytes) and 3D liver microtissues. First, using CellNet, we analyzed whether these liver in vitro cell models were indeed classified as liver, based on their baseline expression profile and gene regulatory networks (GRN). More comprehensive analyses using non-differentially expressed genes (non-DEGs) and differential transcript usage (DTU) were applied to assess the coverage for important liver pathways. Through different analyses, we noticed that 3D liver microtissues exhibited a high similarity with in vivo liver, in terms of CellNet (C/T score: 0.98), non-DEGs (10,363) and pathway coverage (highest for 19 out of 20 liver specific pathways shown) at the beginning of the incubation period (0 h) followed by a decrease during long-term incubation for 168 and 336 h. PHH also showed a high degree of similarity with human liver tissue and allowed stable conditions for a short-term cultivation period of 24 h. Using the same metrics, HepG2 cells illustrated the lowest similarity (C/T: 0.51, non-DEGs: 5623, and pathways coverage: least for 7 out of 20) with human liver tissue. The HepG2 are widely used in hepatotoxicity studies, however, due to their lower similarity, they should be used with caution. HepaRG models, iPSC-HLCs, and hPCLiS ranged clearly behind microtissues and PHH but showed higher similarity to human liver tissue than HepG2 cells. In conclusion, this study offers a resource of RNA-Seq data of several biological replicates of human liver cell models in vitro compared to human liver tissue.
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Affiliation(s)
- Rajinder Gupta
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
| | - Yannick Schrooders
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
| | - Duncan Hauser
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
| | - Marcel van Herwijnen
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
| | - Wiebke Albrecht
- Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), Dortmund, Germany
| | - Bas Ter Braak
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, PO Box 9503, 2300 RA, Leiden, The Netherlands
| | - Tim Brecklinghaus
- Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), Dortmund, Germany
| | - Jose V Castell
- Instituto de Investigación Sanitaria La Fe, Experimental Hepatology Unit, Valencia, Spain
| | - Leroy Elenschneider
- Fraunhofer Institute for Toxicology and Experimental Medicine Preclinical Pharmacology and In-Vitro Toxicology, Nikolai-Fuchs-Straße 1, 30625, Hannover, Germany
| | - Sylvia Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine Preclinical Pharmacology and In-Vitro Toxicology, Nikolai-Fuchs-Straße 1, 30625, Hannover, Germany
| | - Patrick Guye
- InSphero AG, Wagistrasse 27, 8952, Schlieren, Switzerland
| | - Jan G Hengstler
- Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), Dortmund, Germany
| | - Ahmed Ghallab
- Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), Dortmund, Germany
- Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, South Valley University, Qena, 83523, Egypt
| | - Tanja Hansen
- Fraunhofer Institute for Toxicology and Experimental Medicine Preclinical Pharmacology and In-Vitro Toxicology, Nikolai-Fuchs-Straße 1, 30625, Hannover, Germany
| | - Marcel Leist
- In Vitro Toxicology and Biomedicine, Department Inaugurated, Doerenkamp-Zbinden Foundation, University of Konstanz, Konstanz, Germany
| | - Richard Maclennan
- Cyprotex Discovery, No 24 Mereside, Alderley Park, Cheshire, SK10 4TG, UK
| | | | - Laia Tolosa
- Instituto de Investigación Sanitaria La Fe, Unidad Hepatología Experimental, Valencia, Spain
| | - Tine Tricot
- Stem Cell Institute, Department of Development and Regeneration, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Catherine Verfaillie
- Stem Cell Institute, Department of Development and Regeneration, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Paul Walker
- Cyprotex Discovery, No 24 Mereside, Alderley Park, Cheshire, SK10 4TG, UK
| | - Bob van de Water
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, PO Box 9503, 2300 RA, Leiden, The Netherlands
| | - Jos Kleinjans
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
| | - Florian Caiment
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands.
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21
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Chen MJ, Lummertz da Rocha E, Cahan P, Kubaczka C, Hunter P, Sousa P, Mullin NK, Fujiwara Y, Nguyen M, Tan Y, Landry S, Han A, Yang S, Lu YF, Jha DK, Vo LT, Zhou Y, North TE, Zon LI, Daley GQ, Schlaeger TM. Transcriptome Dynamics of Hematopoietic Stem Cell Formation Revealed Using a Combinatorial Runx1 and Ly6a Reporter System. Stem Cell Reports 2020; 14:956-971. [PMID: 32302558 PMCID: PMC7220988 DOI: 10.1016/j.stemcr.2020.03.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 03/18/2020] [Accepted: 03/19/2020] [Indexed: 01/01/2023] Open
Abstract
Studies of hematopoietic stem cell (HSC) development from pre-HSC-producing hemogenic endothelial cells (HECs) are hampered by the rarity of these cells and the presence of other cell types with overlapping marker expression profiles. We generated a Tg(Runx1-mKO2; Ly6a-GFP) dual reporter mouse to visualize hematopoietic commitment and study pre-HSC emergence and maturation. Runx1-mKO2 marked all intra-arterial HECs and hematopoietic cluster cells (HCCs), including pre-HSCs, myeloid- and lymphoid progenitors, and HSCs themselves. However, HSC and lymphoid potential were almost exclusively found in reporter double-positive (DP) cells. Robust HSC activity was first detected in DP cells of the placenta, reflecting the importance of this niche for (pre-)HSC maturation and expansion before the fetal liver stage. A time course analysis by single-cell RNA sequencing revealed that as pre-HSCs mature into fetal liver stage HSCs, they show signs of interferon exposure, exhibit signatures of multi-lineage differentiation gene expression, and develop a prolonged cell cycle reminiscent of quiescent adult HSCs.
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Affiliation(s)
- Michael J Chen
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.
| | - Edroaldo Lummertz da Rocha
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Caroline Kubaczka
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Phoebe Hunter
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Patricia Sousa
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Nathaniel K Mullin
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yuko Fujiwara
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Minh Nguyen
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Yuqi Tan
- Department of Biomedical Engineering, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Samuel Landry
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Areum Han
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Song Yang
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Yi-Fen Lu
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Deepak Kumar Jha
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Linda T Vo
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Yi Zhou
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Trista E North
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Boston, MA, USA
| | - Leonard I Zon
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Boston, MA, USA; Howard Hughes Medical Institute, Harvard University, Boston, MA, USA; Stem Cell and Regenerative Biology Department, Harvard University, Boston, MA, USA
| | - George Q Daley
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Boston, MA, USA
| | - Thorsten M Schlaeger
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Boston, MA, USA.
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22
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Wells CA, Choi J. Transcriptional Profiling of Stem Cells: Moving from Descriptive to Predictive Paradigms. Stem Cell Reports 2020; 13:237-246. [PMID: 31412285 PMCID: PMC6700522 DOI: 10.1016/j.stemcr.2019.07.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/09/2019] [Accepted: 07/10/2019] [Indexed: 12/24/2022] Open
Abstract
Transcriptional profiling is a powerful tool commonly used to benchmark stem cells and their differentiated progeny. As the wealth of stem cell data builds in public repositories, we highlight common data traps, and review approaches to combine and mine this data for new cell classification and cell prediction tools. We touch on future trends for stem cell profiling, such as single-cell profiling, long-read sequencing, and improved methods for measuring molecular modifications on chromatin and RNA that bring new challenges and opportunities for stem cell analysis.
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Affiliation(s)
- Christine A Wells
- Centre for Stem Cell Systems, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville 3010, Australia.
| | - Jarny Choi
- Centre for Stem Cell Systems, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville 3010, Australia
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23
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Steffens S, Fu X, He F, Li Y, Babarinde IA, Hutchins AP. DPre: computational identification of differentiation bias and genes underlying cell type conversions. Bioinformatics 2020; 36:1637-1639. [PMID: 31621827 DOI: 10.1093/bioinformatics/btz789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 10/09/2019] [Accepted: 10/15/2019] [Indexed: 11/12/2022] Open
Abstract
SUMMARY Cells are generally resistant to cell type conversions, but can be converted by the application of growth factors, chemical inhibitors and ectopic expression of genes. However, it remains difficult to accurately identify the destination cell type or differentiation bias when these techniques are used to alter cell type. Consequently, there is demand for computational techniques that can help researchers understand both the cell type and differentiation bias. While advanced tools identifying cell types exist for single cell data and the deconvolution of mixed cell populations, the problem of exploring partially differentiated cells of indeterminate transcriptional identity has not been addressed. To fill this gap, we developed driver-predictor, which relies on scoring per gene transcriptional similarity between RNA-Seq datasets to reveal directional bias of differentiation. By comparing against large cell type transcriptome libraries or a desired target expression profile, the tool enables the user to visualize both the changes in transcriptional identity as well as the genes accounting for the cell type changes. This software will be a powerful tool for researchers to explore in vitro experiments that involve cell type conversions. AVAILABILITY AND IMPLEMENTATION Source code is open source under the MIT license and is freely available on https://github.com/LoaloaF/DPre. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Simon Steffens
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Xiuling Fu
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Fangfang He
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Yuhao Li
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Isaac A Babarinde
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Andrew P Hutchins
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
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Raffin C, Vo LT, Bluestone JA. T reg cell-based therapies: challenges and perspectives. Nat Rev Immunol 2020; 20:158-172. [PMID: 31811270 PMCID: PMC7814338 DOI: 10.1038/s41577-019-0232-6] [Citation(s) in RCA: 388] [Impact Index Per Article: 97.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2019] [Indexed: 12/25/2022]
Abstract
Cellular therapies using regulatory T (Treg) cells are currently undergoing clinical trials for the treatment of autoimmune diseases, transplant rejection and graft-versus-host disease. In this Review, we discuss the biology of Treg cells and describe new efforts in Treg cell engineering to enhance specificity, stability, functional activity and delivery. Finally, we envision that the success of Treg cell therapy in autoimmunity and transplantation will encourage the clinical use of adoptive Treg cell therapy for non-immune diseases, such as neurological disorders and tissue repair.
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Affiliation(s)
- Caroline Raffin
- Sean N. Parker Autoimmune Research Laboratory, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
| | - Linda T Vo
- Sean N. Parker Autoimmune Research Laboratory, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
| | - Jeffrey A Bluestone
- Sean N. Parker Autoimmune Research Laboratory, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA.
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25
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Gam R, Sung M, Prasad Pandurangan A. Experimental and Computational Approaches to Direct Cell Reprogramming: Recent Advancement and Future Challenges. Cells 2019; 8:E1189. [PMID: 31581647 PMCID: PMC6829265 DOI: 10.3390/cells8101189] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 09/26/2019] [Accepted: 10/01/2019] [Indexed: 02/07/2023] Open
Abstract
The process of direct cell reprogramming, also named transdifferentiation, permits for the conversion of one mature cell type directly into another, without returning to a dedifferentiated state. This makes direct reprogramming a promising approach for the development of several cellular and tissue engineering therapies. To achieve the change in the cell identity, direct reprogramming requires an arsenal of tools that combine experimental and computational techniques. In the recent years, several methods of transdifferentiation have been developed. In this review, we will introduce the concept of direct cell reprogramming and its background, and cover the recent developments in the experimental and computational prediction techniques with their applications. We also discuss the challenges of translating this technology to clinical setting, accompanied with potential solutions.
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Affiliation(s)
- Rihab Gam
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK.
| | - Minkyung Sung
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK.
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26
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Tan Y, Cahan P. SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species. Cell Syst 2019; 9:207-213.e2. [PMID: 31377170 DOI: 10.1016/j.cels.2019.06.004] [Citation(s) in RCA: 169] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 04/18/2019] [Accepted: 06/12/2019] [Indexed: 11/28/2022]
Abstract
Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.
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Affiliation(s)
- Yuqi Tan
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Patrick Cahan
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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27
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A systems biology pipeline identifies regulatory networks for stem cell engineering. Nat Biotechnol 2019; 37:810-818. [PMID: 31267104 DOI: 10.1038/s41587-019-0159-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 05/16/2019] [Indexed: 12/18/2022]
Abstract
A major challenge for stem cell engineering is achieving a holistic understanding of the molecular networks and biological processes governing cell differentiation. To address this challenge, we describe a computational approach that combines gene expression analysis, previous knowledge from proteomic pathway informatics and cell signaling models to delineate key transitional states of differentiating cells at high resolution. Our network models connect sparse gene signatures with corresponding, yet disparate, biological processes to uncover molecular mechanisms governing cell fate transitions. This approach builds on our earlier CellNet and recent trajectory-defining algorithms, as illustrated by our analysis of hematopoietic specification along the erythroid lineage, which reveals a role for the EGF receptor family member, ErbB4, as an important mediator of blood development. We experimentally validate this prediction and perturb the pathway to improve erythroid maturation from human pluripotent stem cells. These results exploit an integrative systems perspective to identify new regulatory processes and nodes useful in cell engineering.
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28
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Dunn SJ, Li MA, Carbognin E, Smith A, Martello G. A common molecular logic determines embryonic stem cell self-renewal and reprogramming. EMBO J 2018; 38:embj.2018100003. [PMID: 30482756 PMCID: PMC6316172 DOI: 10.15252/embj.2018100003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 10/03/2018] [Accepted: 10/04/2018] [Indexed: 11/18/2022] Open
Abstract
During differentiation and reprogramming, new cell identities are generated by reconfiguration of gene regulatory networks. Here, we combined automated formal reasoning with experimentation to expose the logic of network activation during induction of naïve pluripotency. We find that a Boolean network architecture defined for maintenance of naïve state embryonic stem cells (ESC) also explains transcription factor behaviour and potency during resetting from primed pluripotency. Computationally identified gene activation trajectories were experimentally substantiated at single‐cell resolution by RT–qPCR. Contingency of factor availability explains the counterintuitive observation that Klf2, which is dispensable for ESC maintenance, is required during resetting. We tested 124 predictions formulated by the dynamic network, finding a predictive accuracy of 77.4%. Finally, we show that this network explains and predicts experimental observations of somatic cell reprogramming. We conclude that a common deterministic program of gene regulation is sufficient to govern maintenance and induction of naïve pluripotency. The tools exemplified here could be broadly applied to delineate dynamic networks underlying cell fate transitions.
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Affiliation(s)
- Sara-Jane Dunn
- Microsoft Research, Cambridge, UK.,Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Meng Amy Li
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Elena Carbognin
- Department of Molecular Medicine, University of Padua, Padua, Italy
| | - Austin Smith
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK .,Department of Biochemistry, University of Cambridge, Cambridge, UK
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29
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Spangler A, Su EY, Craft AM, Cahan P. A single cell transcriptional portrait of embryoid body differentiation and comparison to progenitors of the developing embryo. Stem Cell Res 2018; 31:201-215. [PMID: 30118958 PMCID: PMC6579609 DOI: 10.1016/j.scr.2018.07.022] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 05/28/2018] [Accepted: 07/12/2018] [Indexed: 01/08/2023] Open
Abstract
Directed differentiation of pluripotent stem cells provides an accessible system to model development. However, the distinct cell types that emerge, their dynamics, and their relationship to progenitors in the early embryo has been difficult to decipher because of the cellular heterogeneity inherent to differentiation. Here, we used a combination of bulk RNA-Seq, single cell RNA-Seq, and bioinformatics analyses to dissect the cell types that emerge during directed differentiation of mouse embryonic stem cells as embryoid bodies and we compared them to spatially and temporally resolved transcriptional profiles of early embryos. Our single cell analyses of the day 4 embryoid bodies revealed three populations which had retained related yet distinct pluripotent signatures that resemble the pre- or post-implantation epiblast, one population of presumptive neuroectoderm, one population of mesendoderm, and four populations of neural progenitors. By day 6, the neural progenitors predominated the embryoid bodies, but both a small population of pluripotent-like cells and an anterior mesoderm-like Brachyury-expressing population were present. By comparing the day 4 and day 6 populations, we identified candidate differentiation paths, transcription factors, and signaling pathways that mark the in vitro correlate of the transition from the mid-to-late primitive streak stage.
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Affiliation(s)
- Abby Spangler
- Department of Biomedical Engineering, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Emily Y Su
- Department of Biomedical Engineering, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - April M Craft
- Department of Orthopaedic Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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30
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Dasgupta S, Bader GD, Goyal S. Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics. Biophys J 2018; 115:429-435. [PMID: 30033145 DOI: 10.1016/j.bpj.2018.07.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 06/29/2018] [Accepted: 07/03/2018] [Indexed: 01/04/2023] Open
Abstract
Single-cell genomics has recently emerged as a powerful tool for observing multicellular systems at a much higher level of resolution and depth than previously possible. High-throughput single-cell RNA sequencing techniques are able to simultaneously quantify expression levels of several thousands of genes within individual cells for tens of thousands of cells within a complex tissue. This has led to development of novel computational methods to analyze this high-dimensional data, investigating longstanding and fundamental questions regarding the granularity of cell types, the definition of cell states, and transitions from one cell type to another along developmental trajectories. In this perspective, we outline this emerging field starting from the "input data" (e.g., quantifying transcription levels in single cells), which are analyzed to define "identities" (e.g., cell types, states, and key genes) and to build "interactions" using models that can infer relations and transitions between cells.
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Affiliation(s)
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
| | - Sidhartha Goyal
- Department of Physics, University of Toronto, Toronto, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
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31
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Slamecka J, McClellan S, Wilk A, Laurini J, Manci E, Hoerstrup SP, Weber B, Owen L. Induced pluripotent stem cells derived from human amnion in chemically defined conditions. Cell Cycle 2018; 17:330-347. [PMID: 29143560 DOI: 10.1080/15384101.2017.1403690] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Fetal stem cells are a unique type of adult stem cells that have been suggested to be broadly multipotent with some features of pluripotency. Their clinical potential has been documented but their upgrade to full pluripotency could open up a wide range of cell-based therapies particularly suited for pediatric tissue engineering, longitudinal studies or disease modeling. Here we describe episomal reprogramming of mesenchymal stem cells from the human amnion to pluripotency (AM-iPSC) in chemically defined conditions. The AM-iPSC expressed markers of embryonic stem cells, readily formed teratomas with tissues of all three germ layers present and had a normal karyotype after around 40 passages in culture. We employed novel computational methods to determine the degree of pluripotency from microarray and RNA sequencing data in these novel lines alongside an iPSC and ESC control and found that all lines were deemed pluripotent, however, with variable scores. Differential expression analysis then identified several groups of genes that potentially regulate this variability in lines within the boundaries of pluripotency, including metallothionein proteins. By further studying this variability, characteristics relevant to cell-based therapies, like differentiation propensity, could be uncovered and predicted in the pluripotent stage.
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Affiliation(s)
| | | | - Anna Wilk
- a Mitchell Cancer Institute, University of South Alabama , USA
| | - Javier Laurini
- c College of Medicine, University of South Alabama , Mobile , AL , USA
| | - Elizabeth Manci
- d College of Medicine, University of South Alabama , Mobile , AL , USA
| | - Simon P Hoerstrup
- b Institute for Regenerative Medicine, University of Zurich , Switzerland.,f Center for Applied Biotechnology and Molecular Medicine (CABMM) , University of Zurich - Irchel Campus , Zurich , Switzerland
| | - Benedikt Weber
- b Institute for Regenerative Medicine, University of Zurich , Switzerland.,e Department of Dermatology , University Hospital Zurich , Switzerland.,f Center for Applied Biotechnology and Molecular Medicine (CABMM) , University of Zurich - Irchel Campus , Zurich , Switzerland
| | - Laurie Owen
- g University of California San Diego , La Jolla , CA , USA
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