NanoJ: a high-performance open-source super-resolution microscopy toolbox

Romain F. Laine; Kalina L. Tosheva; Nils Gustafsson; Robert D M Gray; Pedro Almada; David Albrecht; Gabriel Tarrason Risa; Fredrik Hurtig; Ann‐Christin Lindås; Buzz Baum; Jason Mercer; Christophe Leterrier; Pedro M. Pereira; Siân Culley; Ricardo Henriques

doi:10.1088/1361-6463/ab0261

BioinformaticsJournal of Physics D Applied PhysicsJan 28, 2019

Peer-reviewed journal article

NanoJ: a high-performance open-source super-resolution microscopy toolbox

Romain F. Laine iD, Kalina L. Tosheva iD, Nils Gustafsson, Robert D M Gray, Pedro Almada iD, David Albrecht iD, Gabriel Tarrason Risa iD, Fredrik Hurtig iD, Ann‐Christin Lindås iD, Buzz Baum iD, Jason Mercer iD, Christophe Leterrier iD, Pedro M. Pereira iD, Siân Culley iD, Ricardo Henriques iD

Abstract

Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was de-veloped for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image re-construction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.

PDF DOI