Comparing Performance of Spectral Image Analysis Approaches for Detection of Cellular Signals in Time-Lapse Hyperspectral Imaging Fluorescence Excitation-Scanning Microscopy.

Hyperspectral imaging (HSI) technology has been applied in a range of fields for target detection and mixture analysis. While HSI was originally developed for remote sensing applications, modern uses include agriculture, historical document authentication, and medicine. HSI has also shown great utility in fluorescence microscopy. However, traditional fluorescence microscopy HSI systems have suffered from limited signal strength due to the need to filter or disperse the emitted light across many spectral bands. We have previously demonstrated that sampling the fluorescence excitation spectrum may provide an alternative approach with improved signal strength. Here, we report on the use of excitation-scanning HSI for dynamic cell signaling studies-in this case, the study of the second messenger Ca2+. Time-lapse excitation-scanning HSI data of Ca2+ signals in human airway smooth muscle cells (HASMCs) were acquired and analyzed using four spectral analysis algorithms: linear unmixing (LU), spectral angle mapper (SAM), constrained energy minimization (CEM), and matched filter (MF), and the performances were compared. Results indicate that LU and MF provided similar linear responses to increasing Ca2+ and could both be effectively used for excitation-scanning HSI. A theoretical sensitivity framework was used to enable the filtering of analyzed images to reject pixels with signals below a minimum detectable limit. The results indicated that subtle kinetic features might be revealed through pixel filtering. Overall, the results suggest that excitation-scanning HSI can be employed for kinetic measurements of cell signals or other dynamic cellular events and that the selection of an appropriate analysis algorithm and pixel filtering may aid in the extraction of quantitative signal traces. These approaches may be especially helpful for cases where the signal of interest is masked by strong cellular autofluorescence or other competing signals.

Algorithm for biological second messenger analysis with dynamic regions of interest.

Physiological function is regulated through cellular communication that is facilitated by multiple signaling molecules such as second messengers. Analysis of signal dynamics obtained from cell and tissue imaging is difficult because of intricate spatially and temporally distinct signals. Signal analysis tools based on static region of interest analysis may under- or overestimate signals in relation to region of interest size and location. Therefore, we developed an algorithm for biological signal detection and analysis based on dynamic regions of interest, where time-dependent polygonal regions of interest are automatically assigned to the changing perimeter of detected and segmented signals. This approach allows signal profiles to be rigorously and precisely tracked over time, eliminating the signal distortion observed with static methods. Integration of our approach with state-of-the-art image processing and particle tracking pipelines enabled the isolation of dynamic cellular signaling events and characterization of biological signaling patterns with distinct combinations of parameters including amplitude, duration, and spatial spread. Our algorithm was validated using synthetically generated datasets and compared with other available methods. Application of the algorithm to volumetric time-lapse hyperspectral images of cyclic adenosine monophosphate measurements in rat microvascular endothelial cells revealed distinct signal heterogeneity with respect to cell depth, confirming the utility of our approach for analysis of 5-dimensional data. In human tibial arteries, our approach allowed the identification of distinct calcium signal patterns associated with atherosclerosis. Our algorithm for automated detection and analysis of second messenger signals enables the decoding of signaling patterns in diverse tissues and identification of pathologic cellular responses.

Thunderstorms Producing Sferic-Geolocated Gamma-Ray Flashes Detected by TETRA-II.

The terrestrial gamma-ray flash (TGF) and Energetic Thunderstorm Rooftop Array (TETRA-II) detected 22 X-ray/gamma-ray flash events associated with lightning between October 2015 and March 2019 across three ground-based detector locations in subtropical and tropical climates in Louisiana, Puerto Rico, and Panama. Each detector array consists of a set of bismuth germanate scintillators that record X-ray and gamma-ray bursts over the energy range 50 keV-6 MeV (million electron volts). TETRA-II events have characteristics similar to both X-ray bursts associated with lightning leaders and TGFs: sub-millisecond duration, photons up to MeV energies, and association with nearby lightning (typically within 3 km). About 20 of the 22 events are geolocated to individual lightning strokes via spatiotemporally coincident sferics. An examination of radar reflectivity and derived products related to events located within the Next Generation Weather Radar (NEXRAD) monitoring region indicates that events occur within mature cells of severe and non-severe multicellular and squall line thunderstorms, with core echo tops which are at or nearing peak altitude. Events occur in both high lightning frequency thunderstorm cells and low lightning frequency cells. Events associated with high frequency cells occur within 5 min of significant lightning jumps. Among NEXRAD-monitored events, hail is present within 8 km and 5 min of all except a single low-altitude cold weather thunderstorm. An association is seen with maximum thunderstorm development, lightning jumps, and hail cells, indicating that the TETRA-II X-ray/gamma-ray events are associated with the peak storm electrification and development of electric fields necessary for the acceleration of electrons to high energies.

Comparison of spectral FRET microscopy approaches for single-cell analysis.

Förster resonance energy transfer (FRET) is a valuable tool for measuring molecular distances and the effects of biological processes such as cyclic nucleotide messenger signaling and protein localization. Most FRET techniques require two fluorescent proteins with overlapping excitation/emission spectral pairing to maximize detection sensitivity and FRET efficiency. FRET microscopy often utilizes differing peak intensities of the selected fluorophores measured through different optical filter sets to estimate the FRET index or efficiency. Microscopy platforms used to make these measurements include wide-field, laser scanning confocal, and fluorescence lifetime imaging. Each platform has associated advantages and disadvantages, such as speed, sensitivity, specificity, out-of-focus fluorescence, and Z-resolution. In this study, we report comparisons among multiple microscopy and spectral filtering platforms such as standard 2-filter FRET, emission-scanning hyperspectral imaging, and excitation-scanning hyperspectral imaging. Samples of human embryonic kidney (HEK293) cells were grown on laminin-coated 28 mm round gridded glass coverslips (10816, Ibidi, Fitchburg, Wisconsin) and transfected with adenovirus encoding a cAMP-sensing FRET probe composed of a FRET donor (Turquoise) and acceptor (Venus). Additionally, 3 FRET "controls" with fixed linker lengths between Turquoise and Venus proteins were used for inter-platform validation. Grid locations were logged, recorded with light micrographs, and used to ensure that whole-cell FRET was compared on a cell-by-cell basis among the different microscopy platforms. FRET efficiencies were also calculated and compared for each method. Preliminary results indicate that hyperspectral methods increase the signal-to-noise ratio compared to a standard 2-filter approach.