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Ross DH, Bhotika H, Zheng X, Smith RD, Burnum-Johnson KE, Bilbao A. Computational tools and algorithms for ion mobility spectrometry-mass spectrometry. Proteomics 2024; 24:e2200436. [PMID: 38438732 DOI: 10.1002/pmic.202200436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/12/2024] [Accepted: 02/14/2024] [Indexed: 03/06/2024]
Abstract
Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.
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Affiliation(s)
- Dylan H Ross
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Harsh Bhotika
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Xueyun Zheng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Kristin E Burnum-Johnson
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Aivett Bilbao
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
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Maity S, Huang Y, Kilgore MD, Thurmon AN, Vaasjo LO, Galazo MJ, Xu X, Cao J, Wang X, Ning B, Liu N, Fan J. Mapping dynamic molecular changes in hippocampal subregions after traumatic brain injury through spatial proteomics. Clin Proteomics 2024; 21:32. [PMID: 38735925 PMCID: PMC11089002 DOI: 10.1186/s12014-024-09485-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/24/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Traumatic brain injury (TBI) often results in diverse molecular responses, challenging traditional proteomic studies that measure average changes at tissue levels and fail to capture the complexity and heterogeneity of the affected tissues. Spatial proteomics offers a solution by providing insights into sub-region-specific alterations within tissues. This study focuses on the hippocampal sub-regions, analyzing proteomic expression profiles in mice at the acute (1 day) and subacute (7 days) phases of post-TBI to understand subregion-specific vulnerabilities and long-term consequences. METHODS Three mice brains were collected from each group, including Sham, 1-day post-TBI and 7-day post-TBI. Hippocampal subregions were extracted using Laser Microdissection (LMD) and subsequently analyzed by label-free quantitative proteomics. RESULTS The spatial analysis reveals region-specific protein abundance changes, highlighting the elevation of FN1, LGALS3BP, HP, and MUG-1 in the stratum moleculare (SM), suggesting potential immune cell enrichment post-TBI. Notably, established markers of chronic traumatic encephalopathy, IGHM and B2M, exhibit specific upregulation in the dentate gyrus bottom (DG2) independent of direct mechanical injury. Metabolic pathway analysis identifies disturbances in glucose and lipid metabolism, coupled with activated cholesterol synthesis pathways enriched in SM at 7-Day post-TBI and subsequently in deeper DG1 and DG2 suggesting a role in neurogenesis and the onset of recovery. Coordinated activation of neuroglia and microtubule dynamics in DG2 suggest recovery mechanisms in less affected regions. Cluster analysis revealed spatial variations post-TBI, indicative of dysregulated neuronal plasticity and neurogenesis and further predisposition to neurological disorders. TBI-induced protein upregulation (MUG-1, PZP, GFAP, TJP, STAT-1, and CD44) across hippocampal sub-regions indicates shared molecular responses and links to neurological disorders. Spatial variations were demonstrated by proteins dysregulated in both or either of the time-points exclusively in each subregion (ELAVL2, CLIC1 in PL, CD44 and MUG-1 in SM, and SHOC2, LGALS3 in DG). CONCLUSIONS Utilizing advanced spatial proteomics techniques, the study unveils the dynamic molecular responses in distinct hippocampal subregions post-TBI. It uncovers region-specific vulnerabilities and dysregulated neuronal processes, and potential recovery-related pathways that contribute to our understanding of TBI's neurological consequences and provides valuable insights for biomarker discovery and therapeutic targets.
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Affiliation(s)
- Sudipa Maity
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Yuanyu Huang
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Mitchell D Kilgore
- Clinical Neuroscience Research Center, Department of Neurosurgery and Neurology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Abbigail N Thurmon
- Department of Cell and Molecular Biology, Tulane University, New Orleans, LA, USA
- Tulane Brain Institute, New Orleans, LA, USA
| | | | - Maria J Galazo
- Department of Cell and Molecular Biology, Tulane University, New Orleans, LA, USA
- Tulane Brain Institute, New Orleans, LA, USA
| | - Xiaojiang Xu
- Department of Pathology and Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Jing Cao
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiaoying Wang
- Clinical Neuroscience Research Center, Department of Neurosurgery and Neurology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Bo Ning
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Ning Liu
- Clinical Neuroscience Research Center, Department of Neurosurgery and Neurology, Tulane University School of Medicine, New Orleans, LA, USA.
- Tulane University Translational Sciences Institute, New Orleans, LA, USA.
| | - Jia Fan
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA.
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA.
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Sing JC, Charkow J, AlHigaylan M, Horecka I, Xu L, Röst HL. MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization. J Proteome Res 2024. [PMID: 38684072 DOI: 10.1021/acs.jproteome.4c00026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
With the increased usage and diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools, including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity, providing additional insights into the data. The modular framework is easily extendable, enabling rapid algorithm development of novel peak-picker techniques, such as deep-learning-based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.
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Affiliation(s)
- Justin C Sing
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Joshua Charkow
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Mohammed AlHigaylan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Ira Horecka
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Leon Xu
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Hannes L Röst
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario M5G 1A8, Canada
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Manda V, Pavelka J, Lau E. Proteomics applications in next generation induced pluripotent stem cell models. Expert Rev Proteomics 2024; 21:217-228. [PMID: 38511670 PMCID: PMC11065590 DOI: 10.1080/14789450.2024.2334033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 03/08/2024] [Indexed: 03/22/2024]
Abstract
INTRODUCTION Induced pluripotent stem (iPS) cell technology has transformed biomedical research. New opportunities now exist to create new organoids, microtissues, and body-on-a-chip systems for basic biology investigations and clinical translations. AREAS COVERED We discuss the utility of proteomics for attaining an unbiased view into protein expression changes during iPS cell differentiation, cell maturation, and tissue generation. The ability to discover cell-type specific protein markers during the differentiation and maturation of iPS-derived cells has led to new strategies to improve cell production yield and fidelity. In parallel, proteomic characterization of iPS-derived organoids is helping to realize the goal of bridging in vitro and in vivo systems. EXPERT OPINIONS We discuss some current challenges of proteomics in iPS cell research and future directions, including the integration of proteomic and transcriptomic data for systems-level analysis.
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Affiliation(s)
- Vyshnavi Manda
- Department of Medicine, Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Jay Pavelka
- Department of Medicine, Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Edward Lau
- Department of Medicine, Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, USA
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Lee GB, Mazli WNAB, Hao L. Multiomics Evaluation of Human iPSCs and iPSC-Derived Neurons. J Proteome Res 2024. [PMID: 38415376 DOI: 10.1021/acs.jproteome.3c00790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Human induced pluripotent stem cells (iPSCs) can be differentiated into neurons, providing living human neurons to model brain diseases. However, it is unclear how different types of molecules work together to regulate stem cell and neuron biology in healthy and disease states. In this study, we conducted integrated proteomics, lipidomics, and metabolomics analyses with confident identification, accurate quantification, and reproducible measurements to compare the molecular profiles of human iPSCs and iPSC-derived neurons. Proteins, lipids, and metabolites related to mitosis, DNA replication, pluripotency, glycosphingolipids, and energy metabolism were highly enriched in iPSCs, whereas synaptic proteins, neurotransmitters, polyunsaturated fatty acids, cardiolipins, and axon guidance pathways were highly enriched in neurons. Mutations in the GRN gene lead to the deficiency of the progranulin (PGRN) protein, which has been associated with various neurodegenerative diseases. Using this multiomics platform, we evaluated the impact of PGRN deficiency on iPSCs and neurons at the whole-cell level. Proteomics, lipidomics, and metabolomics analyses implicated PGRN's roles in neuroinflammation, purine metabolism, and neurite outgrowth, revealing commonly altered pathways related to neuron projection, synaptic dysfunction, and brain metabolism. Multiomics data sets also pointed toward the same hypothesis that neurons seem to be more susceptible to PGRN loss compared to iPSCs, consistent with the neurological symptoms and cognitive impairment from patients carrying inherited GRN mutations.
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Affiliation(s)
- Gwang Bin Lee
- Department of Chemistry, The George Washington University, 800 22nd St. NW, Washington, D.C. 20052, United States
| | - Wan Nur Atiqah Binti Mazli
- Department of Chemistry, The George Washington University, 800 22nd St. NW, Washington, D.C. 20052, United States
| | - Ling Hao
- Department of Chemistry, The George Washington University, 800 22nd St. NW, Washington, D.C. 20052, United States
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Hertz E, Perez G, Hao Y, Rytel K, Ma C, Kirby M, Anderson S, Wincovitch S, Andersh K, Ahfeldt T, Blanchard J, Qi YA, Lopez G, Tayebi N, Sidransky E, Chen Y. Comparative study of enriched dopaminergic neurons from siblings with Gaucher disease discordant for parkinsonism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581985. [PMID: 38529501 PMCID: PMC10962709 DOI: 10.1101/2024.02.25.581985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Inducible pluripotent stem cells (iPSCs) derived from patient samples have significantly enhanced our ability to model neurological diseases. Comparative studies of dopaminergic (DA) neurons differentiated from iPSCs derived from siblings with Gaucher disease discordant for parkinsonism provides a valuable avenue to explore genetic modifiers contributing to GBA1 -associated parkinsonism in disease-relevant cells. However, such studies are often complicated by the inherent heterogeneity in differentiation efficiency among iPSC lines derived from different individuals. To address this technical challenge, we devised a selection strategy to enrich dopaminergic (DA) neurons expressing tyrosine hydroxylase (TH). A neomycin resistance gene (neo) was inserted at the C-terminus of the TH gene following a T2A self-cleavage peptide, placing its expression under the control of the TH promoter. This allows for TH+ DA neuron enrichment through geneticin selection. This method enabled us to generate comparable, high-purity DA neuron cultures from iPSC lines derived from three sisters that we followed for over a decade: one sibling is a healthy individual, and the other two have Gaucher disease (GD) with GBA1 genotype N370S/c.203delC+R257X (p.N409S/c.203delC+p.R296X). Notably, the younger sister with GD later developed Parkinson disease (PD). A comprehensive analysis of these high-purity DA neurons revealed that although GD DA neurons exhibited decreased levels of glucocerebrosidase (GCase), there was no substantial difference in GCase protein levels or lipid substrate accumulation between DA neurons from the GD and GD/PD sisters, suggesting that the PD discordance is related to of other genetic modifiers.
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Li Z, Weller CA, Shah S, Johnson N, Hao Y, Roberts J, Bereda C, Klaisner S, Machado P, Fratta P, Petrucelli L, Prudencio M, Oskarsson B, Staff NP, Dickson DW, Cookson MR, Ward ME, Singleton AB, Nalls MA, Qi YA. ProtPipe: A Multifunctional Data Analysis Pipeline for Proteomics and Peptidomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571327. [PMID: 38168437 PMCID: PMC10760195 DOI: 10.1101/2023.12.12.571327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, personalized medicine, systems biology and biomedical applications. By combining MS with different proteomics approaches such as immunopurification MS, immunopeptidomics, and total protein proteomics, researchers can gain insights into protein-protein interactions, immune responses, cellular processes, and disease mechanisms. The application of MS-based proteomics in these areas continues to advance our understanding of protein function, cellular signaling, and complex biological systems. Data analysis for mass spectrometry is a critical process that includes identifying and quantifying proteins and peptides and exploring biological functions for these proteins in downstream analysis. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analysis. ProtPipe provides downstream analysis including identifying differential abundance proteins and peptides, pathway enrichment analysis, protein-protein interaction analysis, and MHC1-peptide binding affinity. ProtPipe generates annotated tables and diagnostic visualizations from statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. ProtPipe is well-documented open-source software and is available at https://github.com/NIH-CARD/ProtPipe , accompanied by a web interface.
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Krogsaeter EK, McKetney J, Marquez A, Cakir Z, Stevenson E, Jang GM, Rao A, Zhou A, Huang Y, Krogan NJ, Swaney DL. Lysosomal proteomics reveals mechanisms of neuronal apoE4associated lysosomal dysfunction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560519. [PMID: 37873080 PMCID: PMC10592882 DOI: 10.1101/2023.10.02.560519] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
ApoE4 is the primary risk factor for Alzheimer's Disease. While apoE is primarily expressed by astrocytes, AD pathology including endosomal abnormalities and mitochondrial dysfunction first occurs in neurons. Lysosomes are poised at the convergence point between these features. We find that apoE4-expressing cells exhibit lysosomal alkalinization, reduced lysosomal proteolysis, and impaired mitophagy. To identify driving factors for this lysosomal dysfunction, we performed quantitative lysosomal proteome profiling. This revealed that apoE4 expression results in lysosomal depletion of Lgals3bp and accumulation of Tmed5 in both Neuro-2a cells and postmitotic human neurons. Modulating the expression of both proteins affected lysosomal function, with Tmed5 knockdown rescuing lysosomal alkalinization in apoE4 cells, and Lgals3bp knockdown causing lysosomal alkalinization and reduced lysosomal density in apoE3 cells. Taken together, our work reveals that apoE4 exerts gain-of-toxicity by alkalinizing the lysosomal lumen, pinpointing lysosomal Tmed5 accumulation and Lgals3bp depletion as apoE4-associated drivers for this phenotype.
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Affiliation(s)
- Einar K. Krogsaeter
- Gladstone Data Science and Biotechnology Institute, The J. David Gladstone Institutes, San Francisco, California, USA
- Quantitative Bioscience Institute, University of California, San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
- These authors contributed equally
| | - Justin McKetney
- Gladstone Data Science and Biotechnology Institute, The J. David Gladstone Institutes, San Francisco, California, USA
- Quantitative Bioscience Institute, University of California, San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
- These authors contributed equally
| | - Angelica Marquez
- Gladstone Data Science and Biotechnology Institute, The J. David Gladstone Institutes, San Francisco, California, USA
- Quantitative Bioscience Institute, University of California, San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
| | - Zeynep Cakir
- Gladstone Data Science and Biotechnology Institute, The J. David Gladstone Institutes, San Francisco, California, USA
- Quantitative Bioscience Institute, University of California, San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
| | - Erica Stevenson
- Gladstone Data Science and Biotechnology Institute, The J. David Gladstone Institutes, San Francisco, California, USA
- Quantitative Bioscience Institute, University of California, San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
| | - Gwendolyn M. Jang
- Gladstone Data Science and Biotechnology Institute, The J. David Gladstone Institutes, San Francisco, California, USA
- Quantitative Bioscience Institute, University of California, San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
| | - Antara Rao
- Gladstone Institute of Neurological Disease, The J. David Gladstone Institutes, San Francisco, USA
- Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, USA
| | - Anton Zhou
- Gladstone Institute of Neurological Disease, The J. David Gladstone Institutes, San Francisco, USA
| | - Yadong Huang
- Gladstone Institute of Neurological Disease, The J. David Gladstone Institutes, San Francisco, USA
- Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, USA
- Neuroscience Graduate Program, University of California, San Francisco, USA
- Gladstone Center for Translational Advancement, Gladstone Institutes, San Francisco, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, USA
- Departments of Neurology and Pathology, University of California, San Francisco, USA
| | - Nevan J. Krogan
- Gladstone Data Science and Biotechnology Institute, The J. David Gladstone Institutes, San Francisco, California, USA
- Quantitative Bioscience Institute, University of California, San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
| | - Danielle L. Swaney
- Gladstone Data Science and Biotechnology Institute, The J. David Gladstone Institutes, San Francisco, California, USA
- Quantitative Bioscience Institute, University of California, San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
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