PunctaSpecks: A tool for automated detection, tracking, and analysis of multiple types of fluorescently labeled biomolecules

Abstract

Recent advances in imaging technology and fluorescent probes have made it possible to gain information about the dynamics of subcellular processes at unprecedented spatiotemporal scales. Unfortunately, a lack of automated tools to efficiently process the resulting imaging data encoding fine details of the biological processes remains a major bottleneck in utilizing the full potential of these powerful experimental techniques. Here we present a computational tool, called PunctaSpecks, that can characterize fluorescence signals arising from a wide range of biological molecules under normal and pathological conditions. Among other things, the program can calculate the number, areas, life-times, and amplitudes of fluorescence signals arising from multiple sources, track diffusing fluorescence sources like moving mitochondria, and determine the overlap probability of two processes or organelles imaged using indicator dyes of different colors. We have tested PunctaSpecks on synthetic time-lapse movies containing mobile fluorescence objects of various sizes, mimicking the activity of biomolecules. The robustness of the software is tested by varying the level of noise along with random but known pattern of appearing, disappearing, and movement of these objects. Next, we use PunctaSpecks to characterize protein-protein interaction involved in store-operated Ca²⁺ entry through the formation and activation of plasma membrane-bound ORAI1 channel and endoplasmic reticulum membrane-bound stromal interaction molecule (STIM), the evolution of inositol 1,4,5-trisphosphate (IP₃)-induced Ca²⁺ signals from sub-micrometer size local events into global waves in human cortical neurons, and the activity of Alzheimer's disease-associated β amyloid pores in the plasma membrane. The tool can also be used to study other dynamical processes imaged through fluorescence molecules. The open source algorithm allows for extending the program to analyze more than two types of biomolecules visualized using markers of different colors.

Keywords: Amyloid beta pores; Characterizing fluorescence data; Human cortical neurons; ORAI1-STIM1 interaction; PunctaSpecks; Tracking fluorescence molecules.

Figure 1.

Various steps used by PunctaSpecks for noise removal and puncta identification. (A) Synthetic movie stack containing dynamical activity of objects under investigation (only 5 frames are shown). (B) Maximum intensity profile (MIP) image of the whole movie. (C) Puncta identified by PunctaSpecks using a given algorithm (Otsu in this case) are encircled on the MIP image to ease the visualization and further adjustment of parameters for accurate detection of all puncta. Histograms of the areas (D), mean fluorescence intensity (E) of all puncta, and (F) mean square displacement as a function of time for the mobile puncta.

Figure 2.

Verifying the performance of PunctaSpecks using synthetic data. (A) Frame-by-frame signal to noise ratio of the synthetic data at two different noise levels. Histograms of areas (B), mean open times (log scale) (C), and dwell times (log scale) (D) of all puncta with true (blue) and those from PunctaSpecks at noise levels 25 (green) and 150 (red). (E) Histogram of mean intensities of all puncta with true values (blue) and those from PunctaSpecks at noise level of 25 (green) and 150 (red). The actual trajectory of a sample mobile punctum (blue) and the one identified by PunctaSpecks at noise level of 150 (red) (F).

Figure 3.

Formation of STIM1 and ORAI1 puncta after stimulating HEK293 cells with cyclopiazonic acid. (A-C) Snapshots of the ORAI1 activity at the beginning (before stimulation), middle and end of the experiment (after stimulation). The number of puncta formed (D), mean intensity per punctum (E) and average area per punctum (F) as a function of time for STIM1 (blue) and ORAI1 (red).

Figure 4.

Mobility of STIM1 and ORAI1 after stimulating HEK293 cells with cyclopiazonic acid. An example trajectory (A) and mean square displacement versus time for all mobile STIM1 puncta (B). (D, E) Same as (A, B) for ORAI1. (C) Time-series of the overlap probability of STIM1 and ORAI1 puncta. (F) Mander’s overlap coefficients for STIM1 in ORAI1 (blue) and vice versa (red).

Figure 5.

Comparison of the Otsu, LoG, and DoG methods for Threshold Selection. (A-C) snapshots of the image frames at the beginning, middle and end of the experiment of ORAI1. Time traces of the activity of ORAI in terms of the number of puncta formed (D), mean intensity of puncta (E) and average area per punctum (F). Lower panel (G-I) reports similar data for STIM1.

Figure 6.

Comparison of STIM1 without stimulation and when stimulated with CPA (25 μM). The number (A), mean fluorescence intensity (B), and average area (C) of puncta as a function time in HEK293 cells without any stimulation (black) or treated with CPA (red).

Figure 7.

Time evolution of IP₃-induced hierarchical Ca²⁺ signals in a single human cortical neuron. Example image with a Ca²⁺ blip due to the opening of a single IP₃R and a single puff (A), multiple Ca²⁺ puffs interacting to form a bigger event (B), and global Ca²⁺ wave sweeping the entire neuron towards the end of the experiment (C). The number of events (D) increases till a global Ca²⁺ wave ensues as indicated by the mean area/event (E). Histogram of the event sizes (F, main) fitted by a double exponential function having decay constants A1 and A2 to the small events to indicate the existence of blips and puffs (F, inset).

Figure 8.

Characterizing the activity of Ca²⁺-permeable pores formed by Aβ42 in the plasma membrane of Xenopus laevis oocytes. Identified individual pores at the start (A), middle (B), and end of the experiment (C). Time evolution of the number of puncta (or active pores) (D), their mean intensity (mean change in fluorescence due to a single opening of a pore) (E), and average area per event (the area over which the fluorescence changes due to an opening of a single pore) (F). Histograms of the spatial size (G), dwell times in open state (H), and mean open times (I).