Biology subjects

Grussmayer, K. S.

Publications and source records attributed to Grussmayer, K. S..

5 recordsLinked to original sources

bioRxiv2026

Multiplexed sequence-resolved screening of transient DNA hybridization for programmable nanotechnology

DNA-based technologies rely on short, transient hybridization events, but selecting sequences with desired kinetic properties remains largely empirical because hybridization kinetics are difficult to predict from sequence and slow to measure one sequence at a time. Here, we introduce SPARXS-Hyb, an implementation of SPARXS (Single-molecule Parallel Analysis for Rapid eXploration of Sequence space) for multiplexed sequence-resolved screening of DNA hybridization. Using a surface-immobilized docking-strand library and a quencher-labelled imager-strand library, we screened 128 different DNA sequences in a single kinetic measurement, exposing all sequences to identical experimental conditions. This multiplexed approach removes a major confounding factor of serial measurements, allowing sequence-dependent differences to be compared directly. The resulting dataset reveals sequence-dependent transient binding behaviours and enabled us to identify a sequence with which an order-of-magnitude higher sampling rate can be achieved in DNA-PAINT (DNA points accumulation for imaging in nanoscale topography), a super-resolution microscopy technique based on DNA hybridization. By enabling multiplexed screening across a sequence library, SPARXS-Hyb provides a route to kinetics-guided sequence selection for programmable transient interactions in DNA nanotechnology.

biophysics↗

bioRxiv2024

High-throughput single molecule microscopy with adaptable spatial resolution using exchangeable oligonucleotide labels

Super-resolution microscopy facilitates the visualization of cellular structures at resolutions approaching the molecular level. Especially, super-resolution techniques based on the localization of single-molecules have relatively modest instrument requirements and are thus good candidates for adoption in bioimaging. However, their low throughput nature hampers their applicability in biomolecular research and screening. Here, we propose a workflow for more efficient data collection, starting with scanning of large areas using fast fluctuation based imaging, followed by single-molecule localization microscopy of selected cells. To achieve this workflow, we exploit the versatility of DNA oligo hybridization kinetics with DNA-PAINT probes to tailor the fluorescent blinking towards high-throughput and high-resolution imaging. Additionally, we employ super-resolution optical fluctuation imaging (SOFI) to analyze statistical fluctuations in the DNA-PAINT binding kinetics, thereby tolerating much denser blinking and facilitating accelerated imaging speeds. Thus, we demonstrate 30-300-fold faster imaging of different cellular structures compared to conventional DNA-PAINT imaging, albeit at a lower resolution. Notably, by tuning image medium and data processing though, we can flexibly switch between high-throughput SOFI (scanning a FOV of 0.65mm x 0.52mm within 4 minutes of total acquisition time) and super-resolution DNA-PAINT microscopy and thereby demonstrate that combining DNA-PAINT and SOFI enables to adapt image resolution and acquisition time based to the imaging needs. We envision this approach to be especially powerful when combined with multiplexing and 3D imaging.

biophysics↗

bioRxiv2023

Label-free Identification of Protein Aggregates Using Deep Learning

Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntingtons disease, which is caused by a genetic mutation that leads to a polyglutamine repeat length > 35 in exon 1 of the Huntingtin protein (Httex1). Current research on protein aggregation often involves the use of fluorescent labels to visualize and monitor the dynamics of protein expression, which can alter the biophysical properties of proteins and the final ultrastructure, composition, and toxic properties of the formed aggregates. To overcome this limitation, we present a method for label-free identification of NDD-associated aggregates (LINA). Our approach utilizes deep learning to detect unlabeled and unaltered Httex1 aggregates in living cells from transmitted-light images, without the need for fluorescent labeling. We developed pixel-classification and pixel-regression models, which are robust across imaging conditions, and validated them on aggregates formed by different constructs of Httex1. Our results reveal that Httex1 proteins with shorter polyglutamine repeat lengths form aggregates with a higher average dry mass and area, highlighting the differences in their ultrastructure and aggregation mechanisms. LINA enables the dynamic identification of label-free aggregates and measurement of their dry mass and area changes during their growth process. Our highly-robust models offer high speed, specificity, and simplicity to analyze label-free protein aggregation dynamics and obtain high-fidelity information.

bioengineering↗

bioRxiv2020

Correlative 3D microscopy of single cells using super-resolution and scanning ion-conductance microscopy

High-resolution live-cell imaging is necessary to study complex biological phenomena. Modern fluorescence microscopy methods are increasingly combined with complementary, label-free techniques to put the fluorescence information into the cellular context. The most common high-resolution imaging approaches used in combination with fluorescence imaging are electron microscopy and atomic-force microscopy (AFM), originally developed for solid-state material characterization. AFM routinely resolves atomic steps, however on soft biological samples, the forces between the tip and the sample deform the fragile membrane, thereby distorting the otherwise high axial resolution of the technique. Here we present scanning ion-conductance microscopy (SICM) as an alternative approach for topographical imaging of soft biological samples, preserving high axial resolution on cells. SICM is complemented with live-cell compatible super-resolution optical fluctuation imaging (SOFI). To demonstrate the capabilities of our method we show correlative 3D cellular maps with SOFI implementation in both 2D and 3D with self-blinking dyes for two-color high-order SOFI imaging. Finally, we employ correlative SICM/SOFI microscopy for visualizing actin dynamics in live COS-7 cells with subdiffractional resolution.

biophysics↗

bioRxiv2020

Bioorthogonal red and far-red fluorogenic probes for wash-free live-cell and super-resolution microscopy

Small-molecule fluorophores enable the observation of biomolecules in their native context with fluorescence microscopy. Specific labelling via bioorthogonal tetrazine chemistry confers minimal label size and rapid labelling kinetics. At the same time, fluorogenic tetrazine-dye conjugates exhibit efficient quenching of dyes prior to target binding. However, live-cell compatible long-wavelength fluorophores with strong fluorogenicity have been difficult to realize. Here, we report close proximity tetrazine-dye conjugates with minimal distance between tetrazine and fluorophore. Two synthetic routes give access to a series of cell permeable and impermeable dyes including highly fluorogenic far-red emitting derivatives with electron exchange as dominant excited state quenching mechanism. We demonstrate their potential for live-cell imaging in combination with unnatural amino acids, wash-free multi-colour and super-resolution STED and SOFI imaging. These dyes pave the way for advanced fluorescence imaging of biomolecules with minimal label size.

cell biology↗