H
Heinrich Leonhardt
Researcher at Ludwig Maximilian University of Munich
Publications - 290
Citations - 28420
Heinrich Leonhardt is an academic researcher from Ludwig Maximilian University of Munich. The author has contributed to research in topics: DNA methylation & Chromatin. The author has an hindex of 77, co-authored 280 publications receiving 24755 citations. Previous affiliations of Heinrich Leonhardt include Harvard University & Center for Integrated Protein Science Munich.
Papers
More filters
Journal ArticleDOI
Induction of tumors in mice by genomic hypomethylation.
François Gaudet,J. Graeme Hodgson,Amir Eden,Laurie Jackson-Grusby,Jessica Dausman,Joe W. Gray,Heinrich Leonhardt,Rudolf Jaenisch +7 more
TL;DR: Results indicate that DNA hypomethylation plays a causal role in tumor formation, possibly by promoting chromosomal instability.
Journal ArticleDOI
A guide to super-resolution fluorescence microscopy
TL;DR: These new super-resolution technologies are either based on tailored illumination, nonlinear fluorophore responses, or the precise localization of single molecules and have created unprecedented new possibilities to investigate the structure and function of cells.
Journal ArticleDOI
Subdiffraction Multicolor Imaging of the Nuclear Periphery with 3D Structured Illumination Microscopy
Lothar Schermelleh,Peter M. Carlton,Sebastian Haase,Lin Shao,Lukman Winoto,Peter Kner,Brian Burke,M. Cristina Cardoso,David A. Agard,Mats G. L. Gustafsson,Heinrich Leonhardt,John W. Sedat +11 more
TL;DR: Three-dimensional structured illumination microscopy (3D-SIM) opens new and facile possibilities to analyze subcellular structures beyond the diffraction limit of the emitted light.
Journal ArticleDOI
A targeting sequence directs DNA methyltransferase to sites of DNA replication in mammalian nuclei
TL;DR: Analysis of DNA MTase-beta-galactosidase fusion proteins has shown that association with replication foci is mediated by a novel targeting sequence located near the N-terminus of DNAMTase.
Journal ArticleDOI
Comparative Analysis of Single-Cell RNA Sequencing Methods.
Christoph Ziegenhain,Beate Vieth,Swati Parekh,Björn Reinius,Amy Guillaumet-Adkins,Martha Smets,Heinrich Leonhardt,Holger Heyn,Ines Hellmann,Wolfgang Enard +9 more
TL;DR: Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB- sequencing, and Smart-seq2 are more efficient when analyzing fewer cells.