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Schueder, F.

Publications and source records attributed to Schueder, F..

3 recordsLinked to original sources

Direct visualization of single nuclear pore complex proteins using genetically-encoded probes for DNA-PAINT

The Nuclear Pore Complex (NPC) is one of the largest and most complex protein assemblies in the cell and - among other functions - serves as the gatekeeper of nucleocytoplasmic transport. Unraveling its molecular architecture and functioning has been an active research topic for decades with recent cryogenic electron microscopy and superresolution studies advancing our understanding of the NPC's complex architecture. However, the specific and direct visualization of single copies of NPC proteins and thus the ability to observe single-molecule heterogeneities of these complex structures is thus far elusive. Here, we combine genetically-encoded self-labeling enzymes such as SNAP-tag and HaloTag with DNA-PAINT microscopy. We employ the high localization precision in DNA-PAINT and molecular contrast of these protein tags to optically resolve single copies of nucleoporins in the human Y-complex in three dimensions with a precision of ~3 nm. This technological advancement now enables structural studies of multicomponent complexes on the level of single proteins in cells using optical fluorescence microscopy.

cell biology

Localization microscopy at doubled precision with patterned illumination

MINFLUX offers a breakthrough in single molecule localization precision, but suffers from a tiny field-of-view and a lack of practical parallelism. Here, we combine centroid estimation and illumination pattern induced photon count variations in a conventional widefield imaging setup to extract position information over a typical micron sized field-of-view. We show a near twofold improvement in precision over standard localization with the same photon count on DNA-origami nano-structures.

molecular biology

Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.

biophysics