{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105152"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105152","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Exploring single cell neurochemistry with multimodal MALDI MS","abstract":"Brain function is dependent upon the active coordination of many different cell types and subtypes at cellular resolutions. While measuring neurochemistry at cellular resolutions is important for understanding emergent properties, such as cognition and memory, analyzing the chemical nature of the brain at the single cell level is primarily limited to a handful of analytical approaches which often are only capable of measuring a specific aspect of the cell. For instance, transcriptomics measures the expression level of different genes within a cell, while mass spectrometry (MS) measures gene products and metabolites themselves. Single cells are inherently sample limited, so means of extending or expanding the amount of information that can be garnered from an individual cell is important for understanding their complex neurochemistry. Here, we combine a multitude of analytical approaches with single cell matrixassisted laser desorption/ionization (MALDI) MS to increase the amount of information we can obtain from individual mammalian brain cells. We first developed an open source software, that simplifies microscopy-guided MALDI MS measurements and subsequent correlation of data from orthogonal approaches. Using this software, we analyzed tens of thousands of rodent cerebral cells with MALDI MS and detected over five hundred distinct lipid features. Using this metabolic information, we statistically clustered the cells in 101 unique clusters, while also finding rare lipids only present in a small fraction (<1%) of cells. Further, we extended single cell MALDI MS measurements to accommodate subsequent measurements using immunocytochemistry, infrared spectroscopy, stimulated Raman scattering microscopy, single cell transcriptomics, and capillary electrophoresis, allowing us to merge the rich metabolic information detected with MS to a variety of other systems to maximize the information that can be measured from a single cell.","abstract_html":"Brain function is dependent upon the active coordination of many different cell types and subtypes at cellular resolutions. While measuring neurochemistry at cellular resolutions is important for understanding emergent properties, such as cognition and memory, analyzing the chemical nature of the brain at the single cell level is primarily limited to a handful of analytical approaches which often are only capable of measuring a specific aspect of the cell. For instance, transcriptomics measures the expression level of different genes within a cell, while mass spectrometry (MS) measures gene products and metabolites themselves. Single cells are inherently sample limited, so means of extending or expanding the amount of information that can be garnered from an individual cell is important for understanding their complex neurochemistry. Here, we combine a multitude of analytical approaches with single cell matrixassisted laser desorption/ionization (MALDI) MS to increase the amount of information we can obtain from individual mammalian brain cells. We first developed an open source software, that simplifies microscopy-guided MALDI MS measurements and subsequent correlation of data from orthogonal approaches. Using this software, we analyzed tens of thousands of rodent cerebral cells with MALDI MS and detected over five hundred distinct lipid features. Using this metabolic information, we statistically clustered the cells in 101 unique clusters, while also finding rare lipids only present in a small fraction (&lt;1%) of cells. Further, we extended single cell MALDI MS measurements to accommodate subsequent measurements using immunocytochemistry, infrared spectroscopy, stimulated Raman scattering microscopy, single cell transcriptomics, and capillary electrophoresis, allowing us to merge the rich metabolic information detected with MS to a variety of other systems to maximize the information that can be measured from a single cell.","abstract_has_math":false,"creators":["Neumann, Elizabeth Kathleen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Chemistry","degree_department":null,"school":null,"contributors":["Sweedler, Jonathan V.","Gillette, Martha U.","Kraft, Mary","Rodríguez-López, Joaquín","Dar, Roy"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:44:36Z","date_published":"2019-08-23T20:44:36Z","updated_at":"2026-07-22T22:24:44Z","subjects":["MALDI mass spectrometry","multimodal analysis","single cell analysis"],"languages":["en"],"rights":["Copyright 2019 Elizabeth Neumann"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105152","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sweedler, Jonathan V.","Gillette, Martha U.","Kraft, Mary","Rodríguez-López, Joaquín","Dar, Roy"]},{"key":"dc:creator","label":"Author","values":["Neumann, Elizabeth Kathleen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:44:36Z","2021-08-24T09:15:20Z","2019-03-28","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Chemistry"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["MALDI mass spectrometry","multimodal analysis","single cell analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Elizabeth Neumann"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105152"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Brain function is dependent upon the active coordination of many different cell types and subtypes at cellular resolutions. While measuring neurochemistry at cellular resolutions is important for understanding emergent properties, such as cognition and memory, analyzing the chemical nature of the brain at the single cell level is primarily limited to a handful of analytical approaches which often are only capable of measuring a specific aspect of the cell. For instance, transcriptomics measures the expression level of different genes within a cell, while mass spectrometry (MS) measures gene products and metabolites themselves. Single cells are inherently sample limited, so means of extending or expanding the amount of information that can be garnered from an individual cell is important for understanding their complex neurochemistry. Here, we combine a multitude of analytical approaches with single cell matrixassisted laser desorption/ionization (MALDI) MS to increase the amount of information we can obtain from individual mammalian brain cells. We first developed an open source software, that simplifies microscopy-guided MALDI MS measurements and subsequent correlation of data from orthogonal approaches. Using this software, we analyzed tens of thousands of rodent cerebral cells with MALDI MS and detected over five hundred distinct lipid features. Using this metabolic information, we statistically clustered the cells in 101 unique clusters, while also finding rare lipids only present in a small fraction (<1%) of cells. Further, we extended single cell MALDI MS measurements to accommodate subsequent measurements using immunocytochemistry, infrared spectroscopy, stimulated Raman scattering microscopy, single cell transcriptomics, and capillary electrophoresis, allowing us to merge the rich metabolic information detected with MS to a variety of other systems to maximize the information that can be measured from a single cell.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Elizabeth Neumann, accepted the attached license on 2019-03-26 at 14:24.","The student, Elizabeth Neumann, submitted this Dissertation for approval on 2019-03-26 at 14:28.","This Dissertation was approved for publication on 2019-03-28 at 11:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13454 on 2019-08-22 at 16:20:30","Made available in DSpace on 2019-08-23T20:44:36Z (GMT). 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While measuring neurochemistry at cellular resolutions is important for understanding emergent properties, such as cognition and memory, analyzing the chemical nature of the brain at the single cell level is primarily limited to a handful of analytical approaches which often are only capable of measuring a specific aspect of the cell. For instance, transcriptomics measures the expression level of different genes within a cell, while mass spectrometry (MS) measures gene products and metabolites themselves. Single cells are inherently sample limited, so means of extending or expanding the amount of information that can be garnered from an individual cell is important for understanding their complex neurochemistry. Here, we combine a multitude of analytical approaches with single cell matrixassisted laser desorption/ionization (MALDI) MS to increase the amount of information we can obtain from individual mammalian brain cells. We first developed an open source software, that simplifies microscopy-guided MALDI MS measurements and subsequent correlation of data from orthogonal approaches. Using this software, we analyzed tens of thousands of rodent cerebral cells with MALDI MS and detected over five hundred distinct lipid features. Using this metabolic information, we statistically clustered the cells in 101 unique clusters, while also finding rare lipids only present in a small fraction (<1%) of cells. Further, we extended single cell MALDI MS measurements to accommodate subsequent measurements using immunocytochemistry, infrared spectroscopy, stimulated Raman scattering microscopy, single cell transcriptomics, and capillary electrophoresis, allowing us to merge the rich metabolic information detected with MS to a variety of other systems to maximize the information that can be measured from a single cell.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Elizabeth Neumann, accepted the attached license on 2019-03-26 at 14:24.","The student, Elizabeth Neumann, submitted this Dissertation for approval on 2019-03-26 at 14:28.","This Dissertation was approved for publication on 2019-03-28 at 11:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13454 on 2019-08-22 at 16:20:30","Made available in DSpace on 2019-08-23T20:44:36Z (GMT). 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