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Mukhtar T, Breda J, Adam MA, Boareto M, Grobecker P, Karimaddini Z, Grison A, Eschbach K, Chandrasekhar R, Vermeul S, Okoniewski M, Pachkov M, Harwell CC, Atanasoski S, Beisel C, Iber D, van Nimwegen E, Taylor V. Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis. EMBO J 2022; 41:e111132. [PMID: 36345783 PMCID: PMC9753470 DOI: 10.15252/embj.2022111132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 09/09/2022] [Accepted: 09/26/2022] [Indexed: 11/11/2022] Open
Abstract
The cerebral cortex contains billions of neurons, and their disorganization or misspecification leads to neurodevelopmental disorders. Understanding how the plethora of projection neuron subtypes are generated by cortical neural stem cells (NSCs) is a major challenge. Here, we focused on elucidating the transcriptional landscape of murine embryonic NSCs, basal progenitors (BPs), and newborn neurons (NBNs) throughout cortical development. We uncover dynamic shifts in transcriptional space over time and heterogeneity within each progenitor population. We identified signature hallmarks of NSC, BP, and NBN clusters and predict active transcriptional nodes and networks that contribute to neural fate specification. We find that the expression of receptors, ligands, and downstream pathway components is highly dynamic over time and throughout the lineage implying differential responsiveness to signals. Thus, we provide an expansive compendium of gene expression during cortical development that will be an invaluable resource for studying neural developmental processes and neurodevelopmental disorders.
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Affiliation(s)
- Tanzila Mukhtar
- Department of BiomedicineUniversity of BaselBaselSwitzerland
| | - Jeremie Breda
- BiozentrumUniversity of BaselBaselSwitzerland
- Swiss Institute of Bioinformatics (SIB)BaselSwitzerland
| | - Manal A Adam
- Eli and Edythe Broad Center of Regeneration Medicine and Stem cell ResearchUniversity of California, San FranciscoSan FranciscoCAUSA
- Weill Institute for NeuroscienceSan FranciscoCAUSA
- Department of NeurologyUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Marcelo Boareto
- Swiss Institute of Bioinformatics (SIB)BaselSwitzerland
- Computational Biology Group, D‐BSSEETH ZürichBaselSwitzerland
| | - Pascal Grobecker
- BiozentrumUniversity of BaselBaselSwitzerland
- Swiss Institute of Bioinformatics (SIB)BaselSwitzerland
| | - Zahra Karimaddini
- Swiss Institute of Bioinformatics (SIB)BaselSwitzerland
- Computational Biology Group, D‐BSSEETH ZürichBaselSwitzerland
| | - Alice Grison
- Department of BiomedicineUniversity of BaselBaselSwitzerland
| | - Katja Eschbach
- Department of Biosystems Science and EngineeringETH ZürichBaselSwitzerland
| | | | - Swen Vermeul
- Scientific IT ServicesETH ZürichZürichSwitzerland
| | | | - Mikhail Pachkov
- BiozentrumUniversity of BaselBaselSwitzerland
- Swiss Institute of Bioinformatics (SIB)BaselSwitzerland
| | - Corey C Harwell
- Eli and Edythe Broad Center of Regeneration Medicine and Stem cell ResearchUniversity of California, San FranciscoSan FranciscoCAUSA
- Weill Institute for NeuroscienceSan FranciscoCAUSA
- Department of NeurologyUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Suzana Atanasoski
- Department of BiomedicineUniversity of BaselBaselSwitzerland
- Faculty of MedicineUniversity of ZürichZürichSwitzerland
| | - Christian Beisel
- Department of Biosystems Science and EngineeringETH ZürichBaselSwitzerland
| | - Dagmar Iber
- Swiss Institute of Bioinformatics (SIB)BaselSwitzerland
- Weill Institute for NeuroscienceSan FranciscoCAUSA
| | - Erik van Nimwegen
- BiozentrumUniversity of BaselBaselSwitzerland
- Swiss Institute of Bioinformatics (SIB)BaselSwitzerland
| | - Verdon Taylor
- Department of BiomedicineUniversity of BaselBaselSwitzerland
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Goers R, Coman Schmid D, Jäggi VF, Paioni P, Okoniewski MJ, Parker A, Bangerter B, Georgakopoulou S, Sengstag T, Bielicki J, Tilen R, Vermeul S, Krämer SD, Berger C, Rinn B, Meyer Zu Schwabedissen HE. SwissPK cdw - A clinical data warehouse for the optimization of pediatric dosing regimens. CPT Pharmacometrics Syst Pharmacol 2021; 10:1578-1587. [PMID: 34729953 PMCID: PMC8673996 DOI: 10.1002/psp4.12723] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 09/01/2021] [Accepted: 09/17/2021] [Indexed: 11/10/2022]
Abstract
Clinical trials have been performed mainly in adults and accordingly the necessary information is lacking for pediatric patients, especially regarding dosage recommendation for approved drugs. This gap in information could be filled with results from pharmacokinetic (PK) modeling, based on data collected in daily clinical routine. In order to make this data accessible and usable for research, the Swiss Pharmacokinetics Clinical Data Warehouse (SwissPKcdw) project has been set up, including a clinical data warehouse (CDW) and the regulatory framework for data transfer and use within. Embedded into the secure BioMedIT network, the CDW can connect to various data providers and researchers in order to collaborate on the data securely. Due to its modularity, partially containerized deployment and open‐source software, each of the components can be extended, modified, and re‐used for similar projects that require integrated data management, data analysis, and web tools in a secure scientific data and information technology (IT) environment. Here, we describe a collaborative and interprofessional effort to implement the aforementioned infrastructure between several partners from medical health care and academia. Furthermore, we describe a real‐world use case where blood samples from pediatric patients were analyzed for the presence of genetic polymorphisms and the results were aggregated and further analyzed together with the health‐related patient data in the SwissPKcdw.
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Affiliation(s)
- Roland Goers
- Biopharmacy, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | | | - Vera F Jäggi
- Division of Infectious Diseases and Hospital Epidemiology, University Children's Hospital Zürich, Zürich, Switzerland
| | - Paolo Paioni
- Division of Infectious Diseases and Hospital Epidemiology, University Children's Hospital Zürich, Zürich, Switzerland
| | | | - Althea Parker
- Scientific IT Services, ETH Zürich, Zürich, Switzerland
| | - Beat Bangerter
- Division of Infectious Diseases and Hospital Epidemiology, University Children's Hospital Zürich, Zürich, Switzerland
| | | | - Thierry Sengstag
- Center for Scientific Computing, University of Basel, Basel, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Julia Bielicki
- Pediatric Pharmacology and Pharmacometrics Research, University Children's Hospital Basel, Basel, Switzerland
| | - Romy Tilen
- Biopharmacy, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland.,Division of Infectious Diseases and Hospital Epidemiology, University Children's Hospital Zürich, Zürich, Switzerland
| | - Swen Vermeul
- Scientific IT Services, ETH Zürich, Zürich, Switzerland
| | - Stefanie D Krämer
- Biopharmacy, Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Christoph Berger
- Division of Infectious Diseases and Hospital Epidemiology, University Children's Hospital Zürich, Zürich, Switzerland
| | - Bernd Rinn
- Scientific IT Services, ETH Zürich, Zürich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Khakzad H, Vermeul S, Malmström L. Quaternary Structure Modeling Through Chemical Cross-Linking Mass Spectrometry: Extending TX-MS Jupyter Reports. J Vis Exp 2021. [PMID: 34747410 DOI: 10.3791/60311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Protein-protein interactions can be challenging to study yet provide insights into how biological systems function. Targeted cross-linking mass spectrometry (TX-MS), a method combining quaternary protein structure modeling and chemical cross-linking mass spectrometry, creates high-accuracy structure models using data obtained from complex, unfractionated samples. This removes one of the major obstacles to protein complex structure analysis because the proteins of interest no longer need to be purified in large quantities. Cheetah-MS web server was developed to make the simplified version of the protocol more accessible to the community. Considering the tandem MS/MS data, Cheetah-MS generates a Jupyter Notebook, a graphical report summarizing the most important analysis results. Extending the Jupyter Notebook can yield more in-depth insights and better understand the model and the mass spectrometry data supporting it. The technical protocol presented here demonstrates some of the most common extensions and explains what information can be obtained. It contains blocks to help analyze tandem MS/MS acquisition data and the overall impact of the detected XLs on the reported quaternary models. The result of such analyses can be applied to structural models that are embedded in the notebook using NGLView.
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Affiliation(s)
- Hamed Khakzad
- Equipe Signalisation Calcique et Infections Microbiennes, Ecole Normale Supérieure Paris-Saclay; Institut National de la Santé et de la Recherche Médicale
| | | | - Lars Malmström
- Institute for Computational Science, University of Zurich; S3IT, University of Zurich; Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University;
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