9 research outputs found
ImageJ2: ImageJ for the next generation of scientific image data
ImageJ is an image analysis program extensively used in the biological
sciences and beyond. Due to its ease of use, recordable macro language, and
extensible plug-in architecture, ImageJ enjoys contributions from
non-programmers, amateur programmers, and professional developers alike.
Enabling such a diversity of contributors has resulted in a large community
that spans the biological and physical sciences. However, a rapidly growing
user base, diverging plugin suites, and technical limitations have revealed a
clear need for a concerted software engineering effort to support emerging
imaging paradigms, to ensure the software's ability to handle the requirements
of modern science. Due to these new and emerging challenges in scientific
imaging, ImageJ is at a critical development crossroads.
We present ImageJ2, a total redesign of ImageJ offering a host of new
functionality. It separates concerns, fully decoupling the data model from the
user interface. It emphasizes integration with external applications to
maximize interoperability. Its robust new plugin framework allows everything
from image formats, to scripting languages, to visualization to be extended by
the community. The redesigned data model supports arbitrarily large,
N-dimensional datasets, which are increasingly common in modern image
acquisition. Despite the scope of these changes, backwards compatibility is
maintained such that this new functionality can be seamlessly integrated with
the classic ImageJ interface, allowing users and developers to migrate to these
new methods at their own pace. ImageJ2 provides a framework engineered for
flexibility, intended to support these requirements as well as accommodate
future needs
CellProfiler plugins -- an easy image analysis platform integration for containers and Python tools
CellProfiler is a widely used software for creating reproducible, reusable
image analysis workflows without needing to code. In addition to the >90
modules that make up the main CellProfiler program, CellProfiler has a plugins
system that allows for creation of new modules which integrate with other
Python tools or tools that are packaged in software containers. The
CellProfiler-plugins repository contains a number of these CellProfiler
modules, especially modules that are experimental and/or dependency-heavy.
Here, we present an upgraded CellProfiler-plugins repository with examples of
accessing containerized tools, improved documentation, and added
citation/reference tools to facilitate the use and contribution of the
community.Comment: 17 pages, 2 figures, 1 tabl
FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis.
In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ's routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards
Directed evolution of APEX2 for electron microscopy and proximity labeling
APEX is an engineered peroxidase that functions as an electron microscopy tag and a promiscuous labeling enzyme for live-cell proteomics. Because limited sensitivity precludes applications requiring low APEX expression, we used yeast-display evolution to improve its catalytic efficiency. APEX2 is far more active in cells, enabling the use of electron microscopy to resolve the submitochondrial localization of calcium uptake regulatory protein MICU1. APEX2 also permits superior enrichment of endogenous mitochondrial and endoplasmic reticulum membrane proteins.National Institutes of Health (U.S.) (DP1 OD003961