Semi-automated

Spot detector detects and counts spots, based on wavelet transform.

- Detects spots in noisy images 2D/3D.
- Depending on objective, spots can be nuclei, nucleus or cell
- Versatile input: sequence or batch of file.
- Detects spot in specific band/channel.
- Multi band labeling: automaticaly creates ROIs from one band and count in the same or an other band.
- Filters detection by size.
- Sort detection by ROIs
- Output data in XLS Excel files: number of detection by ROIs, and each detection location and size.
- Outputs withness image with ROIs and detection painted on it.
- Outputs binary detection image.
- Displays detections
- Displays tags

WormGUIDES Atlas is an interactive 4D portrayal of neural development in C. elegans. It will ultimately contain nuclear positions for every cell in the embryo, identified and tracked from the 2 cell stage until hatching. Single-cell and subcellular information, including neural outgrowth dynamics for each cell as well as cell function, gene expression, the adult neural connectome and related literature will be collated for each cell from public sources and also integrated with the atlas model. WormGUIDES Atlas integrates tools for exploratory data analyses and insight sharing. Navigation is linked between 3D and lineage tree views. In both contexts, community single cell information can be accessed with a click, creating live web queries that summarize knowledge about a cell. In many cases this information can be used to control cell color, creating customized interactive visualizations. A user's insights can be annotated directly into the embryo model with a note-taking interface that attaches each annotation to a cell or other point in space and time. These multi-dimensionally located notes can then be ordered into a (chrono)logical story sequence that explains developmental events as they unfold in the embryo. Annotations can be saved and shared with collaborators or the community.

This ImageJ plug-in is a compilation of co-localization tools. It allows:

-Calculating a set of commonly used co-localization indicators:

Pearson's coefficient Overlap coefficient k1 & k2 coefficients Manders' coefficient Generating commonly used visualizations:

-Cytofluorogram

Having access to more recently published methods:

-Costes' automatic threshold

Li's ICA Costes' randomization Objects based methods (2 methods: distances between centres and centre-particle coincidence)

Bio Image Analysis tool from REF

SRRF is a high-performance analytical approach for Live-cell Super-Resolution Microscopy, provided as a fast GPU-enabled ImageJ plugin. SRRF is capable of extracting high-fidelity super-resolution information from TIRF, widefield and confocals using conventional fluorophores such as GFP. SRRF is capable of live-cell imaging over timescales ranging from minutes to hours.