Biology subjects

Streit, M.

Publications and source records attributed to Streit, M..

7 recordsLinked to original sources

bioRxiv2026

Programmable protein reference standards for benchmarking sub-10-nm fluorescence microscopy

Fluorescence microscopy is increasingly used to measure molecular organization at length scales where labeling, photophysics and sample preparation can dominate quantitative accuracy. Reference standards are therefore needed that combine defined nanoscale geometry with a protein-like environment and compatibility with biological imaging. Here we introduce circular tandem repeat protein as programmable protein standards for benchmarking sub-10-nm fluorescence microscopy. Using genetic code expansion and bioorthogonal labeling, we generated compact protein rings carrying up to six labeling sites. Photoswitching fingerprint analysis revealed geometry-dependent localization accumulation and blinking kinetics, demonstrating that short-range fluorophore interactions can be assessed as measurable benchmark parameters. DNA-PAINT confirmed accessible docking sites and programmed valency at the single-particle level. We further established recombinant tethering and genetically encoded membrane display, extending the standards to cellular environments. cTRP PicoRulers were also compatible with expansion microscopy. Together, cTRPs provide a modular protein-based platform for evaluating molecular-scale imaging performance in purified and cellular environments.

biophysics↗

bioRxiv2023

Coronaviruses use ACE2 monomers as entry receptors

The angiotensin-converting enzyme 2 (ACE2) has been identified as entry receptor on cells enabling binding and infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via trimeric spike (S) proteins protruding from the viral surface1,2. It has been suggested that trimeric S proteins preferably bind to plasma membrane areas with high concentrations of preferably multimeric ACE2 receptors to achieve a higher binding and infection efficiency1,3. However, our current knowledge about the influence of ACE2 expression and organization in the plasma membrane on SARS-CoV-2 infection efficiency remains elusive. Here we used direct stochastic optical reconstruction microscopy (dSTORM) in combination with different labeling approaches to visualize the distribution and quantify the expression of ACE2 on different cells. Our results reveal that endogenous ACE2 receptors are present as monomers in the plasma membrane with densities of only 1-2 receptors m-2. In addition, binding of trimeric S proteins does not induce clustering of ACE2 receptors in the plasma membrane. Supported by infection studies using vesicular stomatitis virus (VSV) particles bearing S proteins our data demonstrate that a single S protein interaction per virus particle with a monomeric ACE2 receptor is sufficient for infection which attests SARS-CoV-2 a high infectivity.

molecular biology↗

bioRxiv2022

Kokiri: Random Forest-Based Cohort Comparison and Characterization

AO_SCPLOWBSTRACTC_SCPLOWWe propose an interactive visual analytics approach to characterizing and comparing patient subgroups (i.e., cohorts). Despite having the same disease and similar demographic characteristics, patients respond differently to therapy. One reason for this is the vast number of variables in the genome that influence a patients outcome. Nevertheless, most existing tools do not offer effective means of identifying the attributes that differ most, or look at them in isolation and thus ignore combinatorial effects. To fill this gap, we present Kokiri, a visual analytics approach that aims to separate cohorts based on user-selected data, ranks attributes by their importance in distinguishing between cohorts, and visualizes cohort overlaps and separability. With our approach, users can additionally characterize the homogeneity and outliers of a cohort. To demonstrate the applicability of our approach, we integrated Kokiri into the Coral cohort analysis tool to compare and characterize lung cancer patient cohorts.

bioinformatics↗

bioRxiv2022

Sub-10 nm fluorescence imaging

Advances in superresolution microscopy demonstrated single-molecule localization precisions of a few nanometers. However, translation of such high localization precisions into sub-10 nm spatial resolution in biological samples remains challenging. Here, we show that resonance energy transfer between fluorophores separated by less than 10 nm results in accelerated fluorescence blinking and consequently lower localization probabilities impeding sub-10 nm fluorescence imaging. We demonstrate that time-resolved fluorescence detection in combination with photoswitching fingerprint analysis can be used advantageously to determine the number and distance even of spatially unresolvable fluorophores in the sub-10 nm range. In combination with genetic code expansion (GCE) with unnatural amino acids and bioorthogonal click-labeling with small fluorophores photoswitching fingerprint analysis enables sub-10 nm resolution fluorescence imaging in cells.

biophysics↗

bioRxiv2021

Coral: a web-based visual analysis tool for creating and characterizing cohorts

SummaryA main task in computational cancer analysis is the identification of patient subgroups (i.e., cohorts) based on metadata attributes (patient stratification) or genomic markers of response (biomarkers). Coral is a web-based cohort analysis tool that is designed to support this task: Users can interactively create and refine cohorts, which can then be compared, characterized, and inspected down to the level of single items. Coral visualizes the evolution of cohorts and also provides intuitive access to prevalence information. Furthermore, findings can be stored, shared, and reproduced via the integrated session management. Coral is pre-loaded with data from over 128,000 samples from the AACR Project GENIE, The Cancer Genome Atlas, and the Cell Line Encyclopedia. Availability and ImplementationCoral is publicly available at https://coral.caleydoapp.org. The source code is released at https://github.com/Caleydo/coral. Contactthomas.zichner@boehringer-ingelheim.com Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Allele-specific open chromatin in human iPSC neurons elucidates functional non-coding disease variants

Functional interpretation of noncoding disease variants, which likely regulate gene expression, has been challenging. Chromatin accessibility strongly influences gene expression during neurodevelopment; however, to what extent genetic variants can alter chromatin accessibility in the context of brain disorders/traits is unknown. Using human induced pluripotent stem cell (iPSC)-derived neurons as a neurodevelopmental model, we identified abundant open-chromatin regions absent in adult brain samples and thousands of genetic variants exhibiting allele-specific open-chromatin (ASoC). ASoC variants are overrepresented in brain enhancers, transcription-factor-binding sites, and quantitative-trait-loci associated with gene expression, histone modification, and DNA methylation. Notably, compared to open chromatin regions and other commonly used functional annotations, neuronal ASoC variants showed much stronger enrichments of risk variants for various brain disorders/traits. Our study provides the first snapshot of the neuronal ASoC landscape and a powerful framework for prioritizing functional disease variants.\n\nOne Sentence SummaryAllele-specific open chromatin informs functional disease variants

genetics↗

bioRxiv2018

Ordino: visual analysis tool for ranking and exploring genes, cell lines, and tissue samples

SummaryOrdino is a web-based analysis tool for cancer genomics that allows users to flexibly rank, filter, and explore genes, cell lines, and tissue samples based on pre-loaded data, including The Cancer Genome Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE), and manually uploaded information. Interactive tabular data visualization that facilitates the user-driven prioritization process forms a core component of Ordino. Detail views of selected items complement the exploration. Findings can be stored, shared, and reproduced via the integrated session management.\n\nAvailability and ImplementationOrdino is publicly available at https://ordino.caleydoapp.org. The source code is released at https://github.com/Caleydo/ordino under the Mozilla Public License 2.0.\n\nContactmarc.streit@jku.at

bioinformatics↗