Algorithm for biological second messenger analysis with dynamic regions of interest

Hyperspectral imaging and dynamic region of interest tracking approaches to quantify localized cAMP signals

Cyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger known to orchestrate a myriad of cellular functions over a wide range of timescales. In the last 20 years, a variety of single-cell sensors have been developed to measure second messenger signals including cAMP, Ca2+, and the balance of kinase and phosphatase activities. These sensors utilize changes in fluorescence emission of an individual fluorophore or Förster resonance energy transfer (FRET) to detect changes in second messenger concentration. cAMP and kinase activity reporter probes have provided powerful tools for the study of localized signals. Studies relying on these and related probes have the potential to further revolutionize our understanding of G protein-coupled receptor signaling systems. Unfortunately, investigators have not been able to take full advantage of the potential of these probes due to the limited signal-to-noise ratio of the probes and the limited ability of standard epifluorescence and confocal microscope systems to simultaneously measure the distributions of multiple signals (e.g. cAMP, Ca2+, and changes in kinase activities) in real time. In this review, we focus on recently implemented strategies to overcome these limitations: hyperspectral imaging and adaptive thresholding approaches to track dynamic regions of interest (ROI). This combination of approaches increases signal-to-noise ratio and contrast, and allows identification of localized signals throughout cells. These in turn lead to the identification and quantification of intracellular signals with higher effective resolution. Hyperspectral imaging and dynamic ROI tracking approaches offer investigators additional tools with which to visualize and quantify multiplexed intracellular signaling systems.

Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca2+ Imaging Datasets

Intracellular Ca2+ signals are key for the regulation of cellular processes ranging from myocyte contraction, hormonal secretion, neural transmission, cellular metabolism, transcriptional regulation, and cell proliferation. Measurement of cellular Ca2+ is routinely performed using fluorescence microscopy with biological indicators. Analysis of deterministic signals is reasonably straightforward as relevant data can be discriminated based on the timing of cellular responses. However, analysis of stochastic, slower oscillatory events, as well as rapid subcellular Ca2+ responses, takes considerable time and effort which often includes visual analysis by trained investigators, especially when studying signals arising from cells embedded in complex tissues. The purpose of the current study was to determine if full-frame time-series and line-scan image analysis workflow of Fluo-4 generated Ca2+ fluorescence data from vascular myocytes could be automated without introducing errors. This evaluation was addressed by re-analyzing a published "gold standard" full-frame time-series dataset through visual analysis of Ca2+ signals from recordings made in pulmonary arterial myocytes of en face arterial preparations. We applied a combination of data driven and statistical approaches with comparisons to our published data to assess the fidelity of the various approaches. Regions of interest with Ca2+ oscillations were detected automatically post hoc using the LCPro plug-in for ImageJ. Oscillatory signals were separated based on event durations between 4 and 40 s. These data were filtered based on cutoffs obtained from multiple methods and compared to the published manually curated "gold standard" dataset. Subcellular focal and rapid Ca2+ "spark" events from line-scan recordings were examined using SparkLab 5.8, which is a custom automated detection and analysis program. After filtering, the number of true positives, false positives, and false negatives were calculated through comparisons to visually derived "gold standard" datasets. Positive predictive value, sensitivity, and false discovery rates were calculated. There were very few significant differences between the automated and manually curated results with respect to quality of the oscillatory and Ca2+ spark events, and there were no systematic biases in the data curation or filtering techniques. The lack of statistical difference in event quality between manual data curation and statistically derived critical cutoff techniques leads us to believe that automated analysis techniques can be reliably used to analyze spatial and temporal aspects to Ca2+ imaging data, which will improve experiment workflow.