1
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Murdock MH, Yang CY, Sun N, Pao PC, Blanco-Duque C, Kahn MC, Kim T, Lavoie NS, Victor MB, Islam MR, Galiana F, Leary N, Wang S, Bubnys A, Ma E, Akay LA, Sneve M, Qian Y, Lai C, McCarthy MM, Kopell N, Kellis M, Piatkevich KD, Boyden ES, Tsai LH. Multisensory gamma stimulation promotes glymphatic clearance of amyloid. Nature 2024; 627:149-156. [PMID: 38418876 PMCID: PMC10917684 DOI: 10.1038/s41586-024-07132-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 01/25/2024] [Indexed: 03/02/2024]
Abstract
The glymphatic movement of fluid through the brain removes metabolic waste1-4. Noninvasive 40 Hz stimulation promotes 40 Hz neural activity in multiple brain regions and attenuates pathology in mouse models of Alzheimer's disease5-8. Here we show that multisensory gamma stimulation promotes the influx of cerebrospinal fluid and the efflux of interstitial fluid in the cortex of the 5XFAD mouse model of Alzheimer's disease. Influx of cerebrospinal fluid was associated with increased aquaporin-4 polarization along astrocytic endfeet and dilated meningeal lymphatic vessels. Inhibiting glymphatic clearance abolished the removal of amyloid by multisensory 40 Hz stimulation. Using chemogenetic manipulation and a genetically encoded sensor for neuropeptide signalling, we found that vasoactive intestinal peptide interneurons facilitate glymphatic clearance by regulating arterial pulsatility. Our findings establish novel mechanisms that recruit the glymphatic system to remove brain amyloid.
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Affiliation(s)
- Mitchell H Murdock
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cheng-Yi Yang
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Na Sun
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ping-Chieh Pao
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cristina Blanco-Duque
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Martin C Kahn
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - TaeHyun Kim
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicolas S Lavoie
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matheus B Victor
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Md Rezaul Islam
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fabiola Galiana
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Noelle Leary
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sidney Wang
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Adele Bubnys
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Emily Ma
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Leyla A Akay
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Madison Sneve
- Departments of Biological Engineering and Brain and Cognitive Sciences, McGovern Institute, Cambridge, MA, USA
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yong Qian
- Departments of Biological Engineering and Brain and Cognitive Sciences, McGovern Institute, Cambridge, MA, USA
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cuixin Lai
- School of Life Sciences, Westlake University, Westlake Laboratory of Life Sciences and Biomedicine, and Westlake Institute for Advanced Study, Hangzhou, China
| | - Michelle M McCarthy
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Nancy Kopell
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Manolis Kellis
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kiryl D Piatkevich
- Departments of Biological Engineering and Brain and Cognitive Sciences, McGovern Institute, Cambridge, MA, USA
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- School of Life Sciences, Westlake University, Westlake Laboratory of Life Sciences and Biomedicine, and Westlake Institute for Advanced Study, Hangzhou, China
| | - Edward S Boyden
- Departments of Biological Engineering and Brain and Cognitive Sciences, McGovern Institute, Cambridge, MA, USA
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Li-Huei Tsai
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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2
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Sun N, Victor MB, Park YP, Xiong X, Scannail AN, Leary N, Prosper S, Viswanathan S, Luna X, Boix CA, James BT, Tanigawa Y, Galani K, Mathys H, Jiang X, Ng AP, Bennett DA, Tsai LH, Kellis M. Human microglial state dynamics in Alzheimer's disease progression. Cell 2023; 186:4386-4403.e29. [PMID: 37774678 PMCID: PMC10644954 DOI: 10.1016/j.cell.2023.08.037] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/21/2023] [Accepted: 08/29/2023] [Indexed: 10/01/2023]
Abstract
Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes and epigenomes across 443 human subjects and diverse Alzheimer's disease (AD) pathological phenotypes. We annotate 12 microglial transcriptional states, including AD-dysregulated homeostatic, inflammatory, and lipid-processing states. We identify 1,542 AD-differentially-expressed genes, including both microglia-state-specific and disease-stage-specific alterations. By integrating epigenomic, transcriptomic, and motif information, we infer upstream regulators of microglial cell states, gene-regulatory networks, enhancer-gene links, and transcription-factor-driven microglial state transitions. We demonstrate that ectopic expression of our predicted homeostatic-state activators induces homeostatic features in human iPSC-derived microglia-like cells, while inhibiting activators of inflammation can block inflammatory progression. Lastly, we pinpoint the expression of AD-risk genes in microglial states and differential expression of AD-risk genes and their regulators during AD progression. Overall, we provide insights underlying microglial states, including state-specific and AD-stage-specific microglial alterations at unprecedented resolution.
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Affiliation(s)
- Na Sun
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matheus B Victor
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yongjin P Park
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, BC, Canada; Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Xushen Xiong
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aine Ni Scannail
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Noelle Leary
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shaniah Prosper
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Soujanya Viswanathan
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xochitl Luna
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Carles A Boix
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin T James
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yosuke Tanigawa
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kyriaki Galani
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hansruedi Mathys
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Xueqiao Jiang
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ayesha P Ng
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Li-Huei Tsai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Manolis Kellis
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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3
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Mathys H, Peng Z, Boix CA, Victor MB, Leary N, Babu S, Abdelhady G, Jiang X, Ng AP, Ghafari K, Kunisky AK, Mantero J, Galani K, Lohia VN, Fortier GE, Lotfi Y, Ivey J, Brown HP, Patel PR, Chakraborty N, Beaudway JI, Imhoff EJ, Keeler CF, McChesney MM, Patel HH, Patel SP, Thai MT, Bennett DA, Kellis M, Tsai LH. Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer's disease pathology. Cell 2023; 186:4365-4385.e27. [PMID: 37774677 PMCID: PMC10601493 DOI: 10.1016/j.cell.2023.08.039] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 05/20/2023] [Accepted: 08/29/2023] [Indexed: 10/01/2023]
Abstract
Alzheimer's disease (AD) is the most common cause of dementia worldwide, but the molecular and cellular mechanisms underlying cognitive impairment remain poorly understood. To address this, we generated a single-cell transcriptomic atlas of the aged human prefrontal cortex covering 2.3 million cells from postmortem human brain samples of 427 individuals with varying degrees of AD pathology and cognitive impairment. Our analyses identified AD-pathology-associated alterations shared between excitatory neuron subtypes, revealed a coordinated increase of the cohesin complex and DNA damage response factors in excitatory neurons and in oligodendrocytes, and uncovered genes and pathways associated with high cognitive function, dementia, and resilience to AD pathology. Furthermore, we identified selectively vulnerable somatostatin inhibitory neuron subtypes depleted in AD, discovered two distinct groups of inhibitory neurons that were more abundant in individuals with preserved high cognitive function late in life, and uncovered a link between inhibitory neurons and resilience to AD pathology.
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Affiliation(s)
- Hansruedi Mathys
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA.
| | - Zhuyu Peng
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Carles A Boix
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Matheus B Victor
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Noelle Leary
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Sudhagar Babu
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Ghada Abdelhady
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Xueqiao Jiang
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Ayesha P Ng
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Kimia Ghafari
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Alexander K Kunisky
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Julio Mantero
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kyriaki Galani
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Vanshika N Lohia
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Gabrielle E Fortier
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Yasmine Lotfi
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Jason Ivey
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Hannah P Brown
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Pratham R Patel
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Nehal Chakraborty
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Jacob I Beaudway
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Elizabeth J Imhoff
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Cameron F Keeler
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Maren M McChesney
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Haishal H Patel
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Sahil P Patel
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Megan T Thai
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | | | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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4
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Leary N, Walser S, He Y, Cousin N, Pereira P, Gallo A, Collado‐Diaz V, Halin C, Garcia‐Silva S, Peinado H, Dieterich LC. Melanoma‐derived extracellular vesicles mediate lymphatic remodelling and impair tumour immunity in draining lymph nodes. J Extracell Vesicles 2022; 11:e12197. [PMID: 35188342 PMCID: PMC8859913 DOI: 10.1002/jev2.12197] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
Tumour‐draining lymph nodes (LNs) undergo massive remodelling including expansion of the lymphatic sinuses, a process that has been linked to lymphatic metastasis by creation of a pre‐metastatic niche. However, the signals leading to these changes have not been completely understood. Here, we found that extracellular vesicles (EVs) derived from melanoma cells are rapidly transported by lymphatic vessels to draining LNs, where they selectively interact with lymphatic endothelial cells (LECs) as well as medullary sinus macrophages. Interestingly, uptake of melanoma EVs by LN‐resident LECs was partly dependent on lymphatic VCAM‐1 expression, and induced transcriptional changes as well as proliferation of those cells. Furthermore, melanoma EVs shuttled tumour antigens to LN LECs for cross‐presentation on MHC‐I, resulting in apoptosis induction in antigen‐specific CD8+ T cells. In conclusion, our data identify EV‐mediated melanoma—LN LEC communication as a new pathway involved in tumour progression and tumour immune inhibition, suggesting that EV uptake or effector mechanisms in LECs might represent a new target for melanoma therapy.
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Affiliation(s)
- Noelle Leary
- Institute of Pharmaceutical Sciences Swiss Federal Institute of Technology (ETH) Zurich Zurich Switzerland
| | - Sarina Walser
- Institute of Pharmaceutical Sciences Swiss Federal Institute of Technology (ETH) Zurich Zurich Switzerland
| | - Yuliang He
- Institute of Pharmaceutical Sciences Swiss Federal Institute of Technology (ETH) Zurich Zurich Switzerland
| | - Nikola Cousin
- Institute of Pharmaceutical Sciences Swiss Federal Institute of Technology (ETH) Zurich Zurich Switzerland
| | - Paulo Pereira
- Institute of Pharmaceutical Sciences Swiss Federal Institute of Technology (ETH) Zurich Zurich Switzerland
| | - Alessandro Gallo
- Institute of Pharmaceutical Sciences Swiss Federal Institute of Technology (ETH) Zurich Zurich Switzerland
| | - Victor Collado‐Diaz
- Institute of Pharmaceutical Sciences Swiss Federal Institute of Technology (ETH) Zurich Zurich Switzerland
| | - Cornelia Halin
- Institute of Pharmaceutical Sciences Swiss Federal Institute of Technology (ETH) Zurich Zurich Switzerland
| | - Susana Garcia‐Silva
- Microenvironment and Metastasis Laboratory Spanish National Cancer Research Centre Madrid Spain
| | - Hector Peinado
- Microenvironment and Metastasis Laboratory Spanish National Cancer Research Centre Madrid Spain
| | - Lothar C. Dieterich
- Institute of Pharmaceutical Sciences Swiss Federal Institute of Technology (ETH) Zurich Zurich Switzerland
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5
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Leary N, Walser S, Dieterich LC. Isolation and Fluorescent Labeling of Extracellular Vesicles from Cultured Tumor Cells. Methods Mol Biol 2022; 2504:199-206. [PMID: 35467288 DOI: 10.1007/978-1-0716-2341-1_14] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Extracellular vesicles (EVs), comprising exosomes, ectosomes, and apoptotic bodies, are an important component of molecular cell-to-cell communication, and are critically involved in the pathophysiology of various diseases, including tumors. In order to study the interaction of tumor cell-derived EVs with their target cells and to investigate their biological functions in comparison to other tumor cell-released factors, efficient isolation of EVs from cultured tumor cells, as well as fluorescent labeling of these EVs, is often necessary. In addition, EVs and EV-like particles are emerging as versatile vehicles for the delivery of therapeutic substances. Here, we describe an easy size exclusion chromatography-based method to isolate EVs from the mouse melanoma cell line B16F10 that yields highly enriched EV samples for subsequent applications such as molecular and functional studies. Our protocol also includes an optional labeling step with the lipophilic dye DiD, which allows tracking of EV uptake by recipient cells in vitro and in vivo.
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Affiliation(s)
- Noelle Leary
- Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
| | - Sarina Walser
- Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
| | - Lothar C Dieterich
- Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland.
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6
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Leary N, Matchar B, Gwyther L. BUILDING COMMUNITY WITH PURPOSE: DEMENTIA FAMILIES STAYING CONNECTED ACROSS DISEASE TRAJECTORY. Innov Aging 2018. [DOI: 10.1093/geroni/igy023.569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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7
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Fiscella M, Leary N, Acun E, Ronchi S, Hierlemann A. ELECTROPHYSIOLOGICAL PHENOTYPE CHARACTERIZATION OF HUMAN IPSC-DERIVED DOPAMINERGIC NEURONAL LINES BY MEANS OF HIGH-RESOLUTION MICROELECTRODE ARRAYS. Front Cell Neurosci 2018. [DOI: 10.3389/conf.fncel.2018.38.00014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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8
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Zorzi G, Obien ME, Fiscella M, Leary N, Hierlemann A. Automatic extraction of axonal arbor morphology applied to h-iPSC-derived neurons. Front Cell Neurosci 2018. [DOI: 10.3389/conf.fncel.2018.38.00049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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9
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Obien ME, Zorzi G, Fiscella M, Leary N, Hierlemann A. Comparison of axonal-conduction velocity in developing primary cells and human iPSC-derived neurons. Front Cell Neurosci 2018. [DOI: 10.3389/conf.fncel.2018.38.00095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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10
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Cameron D, Brown K, Grant I, Leary N, Fried M, Lee A. Are we any better at predicting pulmonary artery occlusion pressure? Br J Anaesth 2000. [DOI: 10.1093/bja/84.5.686-a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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