@article {49582, title = {Characterization of neurite dystrophy after trauma by high speed structured illumination microscopy and lattice light sheet microscopy.}, journal = {Journal of Neuroscience Methods}, volume = {312}, year = {2019}, month = {2019 Jan 15}, pages = {154-61}, abstract = {

BACKGROUND: Unbiased screening studies have repeatedly identified actin-related proteins as one of the families of proteins most influenced by neurotrauma. Nevertheless, the status quo model of cytoskeletal reorganization after neurotrauma excludes actin and incorporates only changes in microtubules and intermediate filaments. Actin is excluded in part because it is difficult to image with conventional techniques. However, recent innovations in fluorescent microscopy provide an opportunity to image the actin cytoskeleton at super-resolution resolution in living cells. This study applied these innovations to an in vitro model of neurotrauma.

NEW METHOD: New methods are introduced for traumatizing neurons before imaging them with high speed structured illumination microscopy or lattice light sheet microscopy. Also, methods for analyzing structured illumination microscopy images to quantify post-traumatic neurite dystrophy are presented.

RESULTS: Human induced pluripotent stem cell-derived neurons exhibited actin organization typical of immature neurons. Neurite dystrophy increased after trauma but was not influenced by jasplakinolide treatment. The F-actin content of dystrophies varied greatly from one dystrophy to another.

COMPARISON WITH EXISTING METHODS: In contrast to fixation dependent methods, these methods capture the evolution of the actin cytoskeleton over time in a living cell. In contrast to prior methods based on counting dystrophies, this quantification scheme parameterizes the severity of a given dystrophy as it evolves from a local swelling to an almost-perfect spheroid that threatens to transect the neurite.

CONCLUSIONS: These methods can be used to investigate genetic factors and therapeutic interventions that modulate the course of neurite dystrophy after trauma.

}, issn = {1872-678X}, doi = {10.1016/j.jneumeth.2018.12.005}, author = {Phillips, Jack K and Sherman, Sydney A and Cotton, Kristen Y and Heddleston, John M and Taylor, Aaron B and Finan, John D} } @article {51254, title = {The Pearson{\textquoteright}s correlation coefficient is not a universally superior colocalization metric. Response to {\textquoteright}Quantifying colocalization: the MOC is a hybrid coefficient - an uninformative mix of co-occurrence and correlation{\textquoteright}.}, journal = {Journal of Cell Science}, volume = {132}, year = {2019}, month = {2019 Jan 09}, issn = {1477-9137}, doi = {10.1242/jcs.227074}, author = {Aaron, Jesse S and Taylor, Aaron B and Chew, Teng-Leong} } @article {49043, title = {Image co-localization - co-occurrence versus correlation.}, journal = {Journal of Cell Science}, volume = {131}, year = {2018}, month = {2018 Feb 08}, abstract = {

Fluorescence image co-localization analysis is widely utilized to suggest biomolecular interaction. However, there exists some confusion as to its correct implementation and interpretation. In reality, co-localization analysis consists of at least two distinct sets of methods, termed co-occurrence and correlation. Each approach has inherent and often contrasting strengths and weaknesses. Yet, neither one can be considered to always be preferable for any given application. Rather, each method is most appropriate for answering different types of biological question. This Review discusses the main factors affecting multicolor image co-occurrence and correlation analysis, while giving insight into the types of biological behavior that are better suited to one approach or the other. Further, the limits of pixel-based co-localization analysis are discussed in the context of increasingly popular super-resolution imaging techniques.

}, issn = {1477-9137}, doi = {10.1242/jcs.211847}, author = {Aaron, Jesse S and Taylor, Aaron B and Chew, Teng-Leong} } @article {49085, title = {Model-free quantification and visualization of colocalization in fluorescence images.}, journal = {Cytometry. Part A : the journal of the International Society for Analytical Cytology}, year = {2018}, month = {2018 Mar 13}, abstract = {

The spatial association between fluorescently tagged biomolecules in situ provides valuable insight into their biological relationship. Within the limits of diffraction, such association can be measured using either Pearson{\textquoteright}s Correlation Coefficient (PCC) or Spearman{\textquoteright}s Rank Coefficient (SRC), which are designed to measure linear and monotonic correlations, respectively. However, the relationship between real biological signals is often more complex than these measures assume, rendering their results difficult to interpret. Here, we have adapted methods from the field of information theory to measure the association between two probes{\textquoteright} concentrations based on their statistical dependence. Our approach is mathematically more general than PCC or SRC, making no assumptions about the type of relationship between the probes. We show that when applied to biological images, our measures provide more intuitive results that are also more robust to outliers and the presence of multiple relationships than PCC or SRC. We also devise a display technique to highlight regions in the input images where the probes{\textquoteright} association is higher versus lower. We expect that our methods will allow biologists to more accurately and robustly quantify and visualize the association between two probes in a pair of fluorescence images. {\textcopyright} 2018 International Society for Advancement of Cytometry.

}, issn = {1552-4930}, doi = {10.1002/cyto.a.23356}, author = {Taylor, Aaron B and Ioannou, Maria S and Aaron, Jesse and Chew, Teng-Leong} }