Biology subjects

Helmerich, D. A.

Publications and source records attributed to Helmerich, D. A..

9 recordsLinked to original sources

bioRxiv2026

Two-dye-imager DNA-PAINT enables volumetric nanoscopy of expanded cells

Refined single-molecule localization microscopy methods demonstrated superior localization precisions on isolated sample but remain limited by labeling density and imaging speed in cells. Here we combine expansion microscopy (ExM) with two-dye-imager (TDI)-DNA-PAINT to resolve fine molecular details of protein assemblies in [~]8-fold expanded cells with nanometer resolution. Using lattice light-sheet (LLS) microscopy, Ex-TDI-DNA-PAINT provides a robust platform for three-dimensional (3D) volumetric nanoscopy of the molecular organization of cells.

biophysics↗

bioRxiv2026

Nanoscale imaging of expanded cells and proteins with spontaneously blinking dyes

Expansion microscopy (ExM) enables nanoscale imaging on standard microscopes, but combining ExM with single-molecule localization microscopy (SMLM) remains difficult, owing to the incompatibility of expanded hydrogels with photoswitching buffers. Here, we introduce a single-step expansion microscopy method that allows SMLM with spontaneously blinking dyes in 6-14x expanded samples, without re-embedding. We demonstrate nanometer-resolution imaging by resolving the organization of the nuclear pore complex (NPC) and the molecular structure of recombinant homotrimeric proliferating cell nuclear antigen (PCNA).

biophysics↗

bioRxiv2025

NanTex enables computational multiplexing and phenotyping of organelles across super-resolution modalities

Super-resolution microscopy (SRM) enables nanoscale visualization of cellular organelles but remains constrained by spectral overlap, labeling requirements, and temporal offsets in multicolor imaging. We present NanTex, a deep learning framework that introduces nanotexture as a universal descriptor of subcellular organization and achieves probabilistic demixing of multiple organelles from single-channel SRM images. Unlike segmentation methods that enforce exclusivity, NanTex preserves overlapping morphologies, enabling faithful reconstruction even in crowded regions. Trained on small curated datasets with augmentation, NanTex generalized across SMLM, MINFLUX, STED, SIM, and live-cell Airyscan, with modality-specific retraining where necessary. Demonstrated on cytoskeletal, endomembrane, and metabolic organelles, NanTex achieved high-fidelity reconstructions and extended uniquely to live-cell imaging, where it eliminated temporal misalignment and enabled quantitative tracking of vesicle-like ER subdomains. Notably, NanTex enables quantitative dynamic readouts directly from single-channel live-cell data, revealing and tracking hidden subdomains in real time without the need for additional labels. Beyond multiplexing, NanTex supported computational phenotyping by distinguishing structurally distinct yet molecularly identical populations, exemplified by nocodazole-induced microtubule depolymerization and its glyoxal-mediated modulation. Together, these results establish nanotexture as a paradigm-shifting descriptor for organelle identity and position NanTex as a modality-agnostic, label-efficient, and live-cell-ready strategy for quantitative nanoscale biology. Significance StatementNanTex reframes multiplexing as texture-based demixing, introducing nanotexture as a universal fingerprint of organelle identity. By enabling quantitative, label-efficient reconstructions across SRM modalities and live-cell phenotyping, including vesicle tracking from single-channel data, NanTex opens a path to studying organelle remodeling and disease progression with unprecedented fidelity across SRM modalities.

biophysics↗

bioRxiv2025

Resolving protein organization in cells with nanometer resolution

Advances in super-resolution microscopy have outpaced molecular labeling methods, impeding nanometer-scale imaging of endogenous multiprotein complexes in cells. To overcome these limitations, we developed an expansion microscopy (ExM) approach based on double-homogenized hydrogels that enables direct stochastic optical reconstruction microscopy (dSTORM) of 7-8-fold expanded immunolabeled samples. The resulting [~]4-fold increase in effective immunolabeling density resolved the 8 nm distance between neighboring -tubulin molecules in microtubules and the polyhedral lattice in clathrin-coated pits with nanometer precision in cells. Two-color Ex-dSTORM further revealed the molecular organization of RIM scaffolding protein and Munc13-1, an essential synaptic vesicle priming protein, in ring-like structures with diameters of 40-45 nm at the presynapse in hippocampal neurons. Our results demonstrate that Ex-dSTORM resolves the molecular organization of endogenous multiprotein complexes with nanometer spatial resolution in genetically unmodified cells. Thus, it provides a versatile method for the investigation of molecular protein distributions in their physiologically relevant context. One-Sentence SummaryThe optimized combination of dSTORM and double TREx ExM resolves previously unresolvable protein assemblies in cells

biophysics↗

bioRxiv2023

Fast, whole-cell DNA-PAINT imaging with dual-labeled self-quenched imager probes

Elucidating the interaction between membrane proteins and antibodies requires fast whole-cell imaging at high spatiotemporal resolution. Lattice light-sheet (LLS) microscopy offers fast volumetric imaging but suffers from limited spatial resolution. DNA-PAINT achieves molecular resolution but is practically restricted to two-dimensional imaging due to long acquisition times. Here, we introduce two-dye imager (TDI) probes, manifesting negligible background and amplified fluorescence signal upon transient binding, enabling [~]15-fold faster imaging. Using a combination of TDI-DNA-PAINT and LLS microscopy on B cells, we reveal the oligomeric states and interaction of endogenous CD20 with the therapeutic monoclonal antibody rituximab (RTX), unperturbed by surface effects. Our results demonstrate that B cells become polarized, and microvilli stabilized by RTX binding. These findings, we believe, will aid rational design of improved immunotherapies targeting tumor-associated antigens.

biophysics↗

bioRxiv2022

ReCSAI Recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy

Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method. Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality. Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisiton of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.

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

Isotropic Three-Dimensional Dual-Color Super-Resolution Microscopy with Metal-Induced Energy Transfer

Over the last two decades, super-resolution microscopy has seen a tremendous development in speed and resolution, but for most of its methods, there exists a remarkable gap between lateral and axial resolution. Similar to conventional optical microscopy, the axial resolution is by a factor three to five worse than the lateral resolution. One recently developed method to close this gap is metal-induced energy transfer (MIET) imaging which achieves an axial resolution down to nanometers. It exploits the distance dependent quenching of fluorescence when a fluorescent molecule is brought close to a metal surface. In the present manuscript, we combine the extreme axial resolution of MIET imaging with the extraordinary lateral resolution of single-molecule localization microscopy, in particular with direct stochastic optical reconstruction microscopy (dSTORM). This combination allows us to achieve isotropic three-dimensional super-resolution imaging of sub-cellular structures. Moreover, we employed spectral demixing for implementing dualcolor MIET-dSTORM that allows us to image and co-localize, in three dimensions, two different cellular structures simultaneously.

biophysics↗

bioRxiv2020

Multiple-labeled antibodies behave like single emitters in photoswitching buffer

The degree of labeling (DOL) of antibodies has so far been optimized for high brightness and specific and efficient binding. The influence of the DOL on the blinking performance of antibodies used in direct stochastic optical reconstruction microscopy (dSTORM) has so far attained limited attention. Here, we investigated the spectroscopic characteristics of IgG antibodies labeled at DOLs of 1.1- 8.3 with Alexa Fluor 647 (Al647) at the ensemble and single-molecule level. Multiple-Al647-labeled antibodies showed weak and strong quenching interactions in aqueous buffer but could all be used for dSTORM imaging with spatial resolutions of [~] 20 nm independent of the DOL. Photon antibunching experiments in aqueous buffer demonstrate that the emission of multiple-Al647-labeled antibodies switches from classical to non-classical light in photoswitching buffer. We developed a model that explains the observed blinking of multiple-labeled antibodies and can be used advantageously to develop improved fluorescent probes for dSTORM experiments.

biophysics↗