M
Maxwell A. Sherman
Researcher at Massachusetts Institute of Technology
Publications - 34
Citations - 1720
Maxwell A. Sherman is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Somatic cell & Germline mutation. The author has an hindex of 9, co-authored 26 publications receiving 968 citations. Previous affiliations of Maxwell A. Sherman include Brown University & Broad Institute.
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Journal ArticleDOI
Aging and neurodegeneration are associated with increased mutations in single human neurons
Michael A. Lodato,Michael A. Lodato,Michael A. Lodato,Rachel E. Rodin,Craig L. Bohrson,Michael E. Coulter,Alison R. Barton,Min-Seok Kwon,Maxwell A. Sherman,Carl Vitzthum,Lovelace J. Luquette,Chandri N. Yandava,Pengwei Yang,Thomas Chittenden,Thomas Chittenden,Nicole E. Hatem,Nicole E. Hatem,Nicole E. Hatem,Steven C. Ryu,Steven C. Ryu,Steven C. Ryu,Mollie B. Woodworth,Mollie B. Woodworth,Mollie B. Woodworth,Peter J. Park,Peter J. Park,Christopher A. Walsh,Christopher A. Walsh,Christopher A. Walsh +28 more
TL;DR: The accumulation of somatic mutations with age shows age-related, region- related, and disease-related molecular signatures and may be important in other human age-associated conditions.
Journal ArticleDOI
Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice.
Maxwell A. Sherman,Shane Lee,Robert Law,Saskia Haegens,Saskia Haegens,Catherine A. Thorn,Matti Hämäläinen,Matti Hämäläinen,Christopher I. Moore,Stephanie R. Jones +9 more
TL;DR: A new theory that accounts for the origin of spontaneous neocortical beta is presented and several predictions about optimal states for perceptual and motor performance are made to guide causal interventions to modulate beta for optimal function.
Journal ArticleDOI
Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network
Michael J. McConnell,John V. Moran,Alexej Abyzov,Schahram Akbarian,Taejeong Bae,Isidro Cortes-Ciriano,Jennifer A. Erwin,Liana Fasching,Diane A. Flasch,Donald Freed,Donald Freed,Javier Ganz,Javier Ganz,Andrew E. Jaffe,Kenneth Y. Kwan,Kenneth Y. Kwan,Min-Seok Kwon,Michael A. Lodato,Michael A. Lodato,Ryan E. Mills,Apuã C. M. Paquola,Rachel E. Rodin,Rachel E. Rodin,Chaggai Rosenbluh,Nenad Sestan,Maxwell A. Sherman,Joo Heon Shin,Saera Song,Saera Song,Richard E. Straub,Jeremy Thorpe,Jeremy Thorpe,Daniel R. Weinberger,Alexander E. Urban,Bo Zhou,Fred H. Gage,Thomas Lehner,Geetha Senthil,Christopher A. Walsh,Christopher A. Walsh,Andrew Chess,Eric Courchesne,Joseph G. Gleeson,Joseph G. Gleeson,Jeffrey M. Kidd,Peter J. Park,Jonathan Pevsner,Jonathan Pevsner,Flora M. Vaccarino +48 more
TL;DR: Genomic technologies, including advances in long-read, next-generation DNA sequencing technologies, single-cell genomics, and cutting-edge bioinformatics, can make it possible to determine the types and frequencies of somatic mutations within the human brain.
Posted ContentDOI
Aging and neurodegeneration are associated with increased mutations in single human neurons
Michael A. Lodato,Rachel E. Rodin,Craig L. Bohrson,Michael E. Coulter,Alison R. Barton,Min-Seok Kwon,Maxwell A. Sherman,Carl Vitzthum,Lovelace J. Luquette,Chandri N. Yandava,Pengwei Yang,Thomas Chittenden,Nicole E. Hatem,Steven C. Ryu,Mollie B. Woodworth,Peter J. Park,Christopher A. Walsh +16 more
TL;DR: The accumulation of somatic mutations with age shows age-related, region- related, and disease-related molecular signatures, and may be important in other human age-associated conditions.
Journal ArticleDOI
Linked-read analysis identifies mutations in single-cell DNA-sequencing data.
Craig L. Bohrson,Alison R. Barton,Michael A. Lodato,Rachel E. Rodin,Lovelace J. Luquette,Vinay V Viswanadham,Doga Gulhan,Isidro Cortes-Ciriano,Isidro Cortes-Ciriano,Maxwell A. Sherman,Min-Seok Kwon,Michael E. Coulter,Alon Galor,Christopher A. Walsh,Christopher A. Walsh,Christopher A. Walsh,Peter J. Park +16 more
TL;DR: Linked-read analysis is a method for analyzing single-cell DNA-sequencing data that accurately identifies somatic single-nucleotide variants by using read-level phasing with nearby germline variants, enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.