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DeepFinder is a deep learning-based tool for identifying macromolecules in cellular cryo-electron tomograms. DeepFinder performs with an accuracy comparable to expert-supervised ground truth annotations on multiple experimental datasets.
DeepInterpolation is a self-supervised deep learning-based denoising approach for calcium imaging, electrophysiology and fMRI data. The approach increases the signal-to-noise ratio and allows extraction of more information from the processed data than from the raw data.
A compact adaptive optics module corrects aberrations in two-photon and three-photon microscopy, enabling structural and functional imaging deep in the mouse brain, the mouse spinal cord and the zebrafish larva.
An iSCAT image processing and analysis strategy enables mass-sensitive particle tracking (MSPT) of single unlabeled biomolecules on a supported lipid bilayer. MSPT was used to observe the (dis-)assembly of membrane complexes in real-time.
This work describes inCITE-seq that jointly measures intranuclear protein levels and the transcriptome in single nuclei, which is applied to mouse brain tissue to relate quantitative protein levels of TFs to gene expression programs.
Dynamic mass photometry allows label-free tracking and mass measurement of individual membrane-associated proteins diffusing on supported lipid bilayers. The approach can be used to monitor dynamic (dis)assembly of protein complexes.
By using a new deep learning architecture, Enformer leverages long-range information to improve prediction of gene expression on the basis of DNA sequence.
SEAM is a platform for the analysis of high-resolution secondary ion mass spectrometry imaging that allows spatially resolved nuclear metabolomic profiling at the single-cell level.
This work describes the generation of a modification-free RNA library that resembles endogenous transcriptome sequence and expression level, which can be used as a negative control in epitranscriptomic sequencing methods to obtain high-confidence and quantitative maps of various RNA modifications.
Three-photon microscopy in combination with adaptive optics-based aberration correction and ECG-triggered gating allows high-resolution imaging of neurons and astrocytes up to a depth of 1.4 mm in the mouse brain.
This work describes nascent Ribo-Seq, which measures the speed of ribosome loading and polysome assembly on endogenous mRNA through a combination of 4-thiouridine labeling and ribosome footprint sequencing.
This work describes m6A-seq2, which utilizes multiplexed N6-methyladenosine (m6A) immunoprecipitation of pre-barcoded and pooled samples to reduce the technical variability and allows direct comparison of m6A at the site, gene and sample level.
DECODE uses deep learning for localizing single emitters in high-density two-dimensional and three-dimensional single-molecule localization microscopy data. DECODE outperforms available methods and enables fast live-cell SMLM of dynamic processes.
Mitometer enables efficient, rapid, and accurate automated segmentation and tracking of mitochondria from time-lapse images. Mitometer performs well on diverse input images and can be used to monitor dynamic fission and fusion events.
DeepCAD is a self-supervised deep-learning approach for denoising calcium imaging data. DeepCAD improved SNR and facilitates neuron extraction and spike inference.