dc.contributor.author | Shen, Fred Y | |
dc.contributor.author | Harrington, Margaret M | |
dc.contributor.author | Walker, Logan A | |
dc.contributor.author | Cheng, Hon Pong Jimmy | |
dc.contributor.author | Boyden, Edward S | |
dc.contributor.author | Cai, Dawen | |
dc.date.accessioned | 2021-10-27T20:23:26Z | |
dc.date.available | 2021-10-27T20:23:26Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/135429 | |
dc.description.abstract | © 2020, The Author(s). Mapping neuroanatomy is a foundational goal towards understanding brain function. Electron microscopy (EM) has been the gold standard for connectivity analysis because nanoscale resolution is necessary to unambiguously resolve synapses. However, molecular information that specifies cell types is often lost in EM reconstructions. To address this, we devise a light microscopy approach for connectivity analysis of defined cell types called spectral connectomics. We combine multicolor labeling (Brainbow) of neurons with multi-round immunostaining Expansion Microscopy (miriEx) to simultaneously interrogate morphology, molecular markers, and connectivity in the same brain section. We apply this strategy to directly link inhibitory neuron cell types with their morphologies. Furthermore, we show that correlative Brainbow and endogenous synaptic machinery immunostaining can define putative synaptic connections between neurons, as well as map putative inhibitory and excitatory inputs. We envision that spectral connectomics can be applied routinely in neurobiology labs to gain insights into normal and pathophysiological neuroanatomy. | |
dc.language.iso | en | |
dc.publisher | Springer Science and Business Media LLC | |
dc.relation.isversionof | 10.1038/S41467-020-18422-8 | |
dc.rights | Creative Commons Attribution 4.0 International license | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Nature | |
dc.title | Light microscopy based approach for mapping connectivity with molecular specificity | |
dc.type | Article | |
dc.contributor.department | McGovern Institute for Brain Research at MIT | |
dc.contributor.department | Koch Institute for Integrative Cancer Research at MIT | |
dc.contributor.department | Program in Media Arts and Sciences (Massachusetts Institute of Technology) | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Biological Engineering | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences | |
dc.contributor.department | Howard Hughes Medical Institute | |
dc.relation.journal | Nature Communications | |
dc.eprint.version | Final published version | |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
eprint.status | http://purl.org/eprint/status/PeerReviewed | |
dc.date.updated | 2021-03-23T17:20:35Z | |
dspace.orderedauthors | Shen, FY; Harrington, MM; Walker, LA; Cheng, HPJ; Boyden, ES; Cai, D | |
dspace.date.submission | 2021-03-23T17:20:37Z | |
mit.journal.volume | 11 | |
mit.journal.issue | 1 | |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | |