Danuser Lab's Projects
3dCellShape implements measurement and analysis of geometric features on extracted 3D volumes of single cells from fluorescent imaging data.
This is a project of a membrane model
clathrin-mediated endocytosis analysis
DeBias decouples global bias from direct interactions in co-orientation and co-localization data.
Python code used to detect and track blebs frame-to-frame, and to analyze bleb size statistics before and after photoactivation.
This focal adhesion package is a software that tracks, segments, classifies and analyzes the time series of focal- and nascent adhesions in an adhesion time-lapse images.
Growth Cone Analyzer pipeline
Fine-grained, nonlinear registration of live cell movies reveals spatiotemporal organization of diffuse molecular processes
Monolayer spatiotemporal dynamics analysis.
Profiling cellular morphodynamics by spatiotemporal spectrum decomposition.
Live Cell Histology: Extracting latent features from label-free live cell images using Adversarial Autoencoders
The usage of the protrusion/retraction package
Quantitative Fluorescent Speckle Microscopy
A Matlab package for segmentation of filament and the orientation.
u-inferforce (Traction Force Microscopy) is a MATLAB software that reconstructs traction forces of cells adhered on elastic gel doped with beads.
Compute causal relationships between individual pixels in 2D videos over space and time to reveal salient dynamics using a variety of causal measures
Pipeline for inference of Granger-causal relations in molecular systems to study actin regulation in lamellipodia
Processing of raw ratiometric biosensor images (for example based on FRET) into fully corrected "ratio maps" or "activation maps" — images showing the localized activation of the biosensor.
Analyze local cell edge motions (e.g. protrusion and retraction) and to locally sample intracellular fluorescence signals in 2D fluorescence microscopy data.
A Matlab software package to do 2D cell segmentation.
Generate consensus 3D cells segmentations by combining 2D cell segmentations from any combination of xy, xz, yz views, compatible with outputs of any 2D segmentation method.
Detect morphological motifs, such as blebs, filopodia, and lamellipodia, from 3D images of surfaces, particularly images of cell surfaces.
The computational platform u-signal3D defines a shape-invariant representation of the spatial scales of molecular organization at the cell surface, enabling comparison and machine-learning of signaling across morphologically diverse cell populations.
Multiple-particle tracking designed to (1) track dense particle fields, (2) close gaps in particle trajectories resulting from detection failure, and (3) capture particle merging and splitting events resulting from occlusion or genuine aggregation and dissociation events
Multiple particle tracking in dense 3D particle fields complemented with dynamic regions of interest and trackability inferences for the automated exploration of large volumetric sequences.
Transform 3D cell surfaces into different representations including topographic maps, 3D spheres, and 2D images for doing optimized quantification, data analysis and machine learning.
Several post-processing scripts that analyze signal distributions on and near uShape3D-generated cell surfaces.