A dataset related to a manuscript “Structured excitability: origin of spiral wave revealed by trans-scale scope”
Abstract
Spiral waves are typical dynamics in signal relaying excitable systems and are associated with pathological cases such as neuronal epilepsy and cardiac fibrillation. Despite rich knowledge of “induced” spirals under designed conditions, the mechanisms of “spontaneous” spiral wave formation are elusive due to technical difficulties in analyzing rare spiral formation in naïve tissues. Here, we investigated the development of social amoeba, another model of living excitable systems, which invariably self-organizes spiral waves of intercellular communication from a non-signaling state. Trans-scale imaging of signaling dynamics in ~130,000 cells revealed that macroscale spiral waves were formed by reentry, a classical hypothesis for cardiac fibrillation, relying on mesoscopically structured excitability as a scaffold. Experimental and computational analysis elucidated that robust reentry was facilitated by anisotropic and graded development of the structured excitability that spatially and temporally increased the chance of reentry, which might be commonly involved in the formation of disease-associated spirals.
Molecular biology. cDNA encoding mRFPmars-Flamindo2 fusion protein (Red-FL2), for which codon usage was optimized for D. discoideum, was constructed using the inFusion cloning system (TAKARA) and cloned into pDM304 and pDM358. The resulting plasmids, pDM304_Red-FL2 and pDM358_Red-FL2, were deposited at the Dicty Stock Center.
Cell culture. The axenic strain Ax2 was cultured and transformed as described elsewhere (47, 48). For transformation, cells were washed and suspended with ice-cold EP buffer (6.6 mM KH2PO4, 2.8 mM Na2HPO4, 50 mM sucrose, pH 6.4) at 1 × 107 cells/ml. An 800-µl cell suspension mixed with 10 µg pDM304_Red-FL2 in a 4-mm-wide cuvette was subject to electroporation (two 5-s separated pulses with 1.0 kV and a 1.0 ms time constant) using a MicroPulser (Bio-Rad). These cells were plated on 4–6 90-mm plastic dishes with HL5 medium, incubated at 22°C for 18 h under non-selective conditions, and then cultured in the presence of 10 µg/ml G418 (Wako). After 4–7 days, colonies with high expression of Red-FL2 were manually picked. Some of these colonies were subsequently transformed with pDM358_Red-FL2 and cultured in the presence of 35 µg/ml hygromycin (Wako) and 15 µg/ml G418. Clones showing higher expression of Red-FL2 with lower heterogeneity were screened.
Imaging. Cells expressing Red-FL2 were maintained in HL5 medium on a 90-mm plastic dish at a density of <1 × 106 cells/dish. Cell development was initiated by three washes with development buffer (5 mM Na2HPO4, 5 mM KH2PO4, 1 mM CaCl2, 2 mM MgCl2, pH 6.4). Cells were then plated on a 35-mm glass-bottom dish at a density of ~850 cells/mm2, which was the highest density allowing reliable 1-cell tracking. Live-cell imaging was performed using a custom-built imaging system equipped with a single CMOS image sensor and LED illumination, called AMATERAS (a multi-scale/modal analytical tool for every rare activity in singularity), that allowed taking a snapshot of 130,000 cells/14.6 × 10.1 mm2 with a spatial resolution of 2.3 µm (the details of the imaging system is described elsewhere (Ref 1). Cell density in reset experiments was doubled for better visibility.
Image analysis. Ratio images of background-subtracted and spatially smoothed channels for RFP and Flamindo2 were subject to image enhancement of pulsed cells assisted by supervised machine learning using AIVIA software (DRVISION tech.). The image field was subdivided into 12,236 analysis-ROIs (133 × 92 matrices of 100 × 100 pixels), and peak detection was performed on time series data for every ROI using Mathematica.Wavefront, oscillation phase, and cumulative pulse counts were analyzed in each analysis-ROI containing ~10 cells. Image reconstruction from these data was performed with a custom-built analysis pipeline using Excel, Mathematica, MATLAB, and Fiji software. For peak detection, sensitivity was tuned to detect more than two pulsing cells/analysis-ROI. After obtaining a peak table for 900 frames of 12,236 analysis-ROIs, those from the left half of the full-FOV were manually corrected to detect one pulsing cell/analysis-ROI with a Δratio of Red/FL2 (ΔR) >5%.
Computational analysis. The genetic feedback model is a hybrid cellular automaton (CA) in which the reaction-diffusion dynamics of the extracellular cAMP (c) and the pulse-dependent increase in the excitability (Ex) obey the following(Refs 2,3,4). A modified model was constructed in this study to recapitulate the realistic development of Ex. The first modification was an anisotropic development of Ex realized by a spatially heterogeneous initial condition of Ex, and the other was the gradual development of cAMP amplitude realized by Ex-dependent cAMP synthesis with a moderate feedback strength on Ex. The analysis was performed using MATLAB.
Quantification of the cAMP pulse at 1-cell. To quantitatively compare the amount of cAMP pulse among cells, we performed two-step normalization for ΔR. Considering the identical stoichiometry of red and yellow signals of Red-FL2 among cells, we pre-normalized baseline ratio values to 1.0, which cancelled the different baseline ratios of distantly positioned cells caused by a slight imbalance of illumination strength over the image field. Maximum values of pre-normalized ΔR were separately determined to be 5.5 for >300 cells by prolonged stimulation with 8-Br-cAMP (20 mM), a cell-permeable cAMP analog (data not shown) (33). Finally, we normalized pulse data from all examined cells by transforming minimum (1.0) and maximum (5.5) values of pre-normalized ΔR to 0 and 1, respectively.
Refs
1. T. Ichimura et al., Trans-scale-scope to find rare cellular activity in sub-million cells. bioRxiv, 2020.2006.2029.179044 (2020). DOI:10.1101/2020.06.29.179044
2. H. Levine, I. Aranson, L. Tsimring, T. V. Truong, Positive genetic feedback governs cAMP spiral wave formation in Dictyostelium. Proc Natl Acad Sci U S A 93, 6382-6386 (1996). DOI: 10.1073/pnas.93.13.6382
3. D. Geberth, M. T. Hutt, Predicting spiral wave patterns from cell properties in a model of biological self-organization. Phys Rev E Stat Nonlin Soft Matter Phys 78, 031917 (2008). DOI: 10.1103/PhysRevE.78.031917
4. S. Sawai, P. A. Thomason, E. C. Cox, An autoregulatory circuit for long-range self-organization in Dictyostelium cell populations. Nature 433, 323-326 (2005). DOI: 10.1038/nature03228
Taishi Kakizuka, Hidenori Nakaoka, Yusuke Hara, Aya Ichiraku, Yoshiyuki Arai, Hiroya Itoga, Shuichi Onami, Taro Ichimura, Takeharu Nagai, Kazuki Horikawa (2025) Mesoscale heterogeneity is a critical determinant for spiral pattern formation in developing social amoeba., Scientific reports, Volume 15, Number 1, pp. 1422
Published in 2025 Jan 9 (Electronic publication in Jan. 9, 2025, midnight )
(Abstract) Heterogeneity is a critical determinant for multicellular pattern formation. Although the importance of microscale and macroscale heterogeneity at the single-cell and whole-system levels, respectively, has been well accepted, the presence and functions of mesoscale heterogeneity, such as cell clusters with distinct properties, have been poorly recognized. We investigated the biological importance of mesoscale heterogeneity in signal-relaying abilities (excitability) in the self-organization of spiral waves of intercellular communications by studying the self-organized pattern formation in a population of Dictyostelium discoideum cells, a classical signal-relaying system model. By utilizing pulse-count analysis to evaluate cellular excitability, we successfully visualized the development of mesoscale heterogeneity in excitability, whose spatial scale was comparably large to that of the traveling waves of intercellular communication. Together with perturbation experiments, our detailed analysis of the structural change in mesoscale heterogeneity and associated wave dynamics demonstrated the functional importance of mesoscale heterogeneity in generating the spiral wave pattern, whose experimental observations were first realized. We propose that mesoscale heterogeneity, in addition to microscale and macroscale heterogeneities, is a critical determinant of diverse multicellular pattern formations.(MeSH Terms)