First experimental measurements of biophotons from Astrocytes and Glioblastoma cell cultures

TL;DR

This study reports the first measurements of endogenous biophotons from astrocyte and glioblastoma cultures using two ultra-sensitive, dark-condition setups, revealing clear separation from dark noise. By applying Diffusion Entropy Analysis with a stripes-enhanced approach, the authors uncover anomalous diffusion and long-range memory in the biophoton time series, with astrocytes and glioblastoma displaying distinct dynamical patterns. The work demonstrates that biophoton emissions carry information about cellular metabolism and pathology beyond simple intensity, offering a potential non-invasive diagnostic window and a framework for studying cellular communication. The findings lay groundwork for extending this approach to neuronal cultures and motivating hardware optimizations to boost detection efficiency and geometric coverage.

Abstract

Biophotons are non-thermal and non-bioluminescent ultraweak photon emissions, first hypothesised by Gurwitsch in 1924 as a regulatory mechanism in cell division, and then experimentally observed in living organisms. Today, two main hypotheses explain their origin: stochastic decay of excited molecules and coherent electromagnetic fields produced in biochemical processes. Recent interest focuses on the role of biophotons in cellular communication and disease monitoring. This study presents the first campaign of biophoton emission measurements from cultured astrocytes and glioblastoma cells, conducted at Fondazione Pisana per la Scienza (FPS) using two ultra-sensitive setups developed by the collaboration at the National Laboratories of Frascati (LNF-INFN) and at the University of Rome II - Tor Vergata. The statistical analyses of the data collected revealed a clear separation between cellular signals and dark noise, confirming the high sensitivity of the apparatuses. The Diffusion Entropy Analysis (DEA) was applied to the data to uncover dynamic patterns, revealing anomalous diffusion and long-range memory effects potentially related to intercellular signalling and cellular communication. These findings support the hypothesis that biophoton emissions encode rich information beyond intensity, reflecting metabolic and pathological states. The differences that emerged from the application of Diffusion Entropy Analysis to the biophotonic signals of Astrocytes and Glioblastoma are highlighted and discussed in the paper. This work lays the foundation for future studies on neuronal cultures and proposes biophoton dynamics as a promising tool for non-invasive diagnostics and cellular communication research.

Figures (6)

• Figure 1: Pictures of the final setup installed at the "Fondazione Pisana per la Scienza" (Pisa, Italy) for first measurements of biophoton emissions from cell cultures. On the left, the two setups installed inside the incubator are shown. On the right, the incubator is shielded from external light, with the two measuring devices inside.
• Figure 2: Comparison plots of biophotonic signals emitted by astrocyte (green) and glioblastoma (red) cell cultures concerning the background (dark counts in blue) performed with the LNF machine at the laboratories of Fondazione Pisana per la Scienza (FPS).
• Figure 3: Comparison plots of biophotonic signals emitted by astrocyte (green) and glioblastoma (red) cell cultures concerning the background (dark counts in blue) performed with the TOV machine at the laboratories of Fondazione Pisana per la Scienza (FPS).
• Figure 4: Comparison between the count distribution function for astrocyte emission in the LNF experimental setup and the best fit performed with a Poisson function. The mean value of the best fit is $<m>=22.6$ compared to the experimental value $<m>=22.82$. The vertical black line defines the part of the distribution above the quantile $q=0.9$..
• Figure 5: Graph of $S(l)$ vs $\ln l$ obtained with the Diffusion Entropy Analysis (DEA) with stripes on the data collected with the LNF machine measuring biophotons emitted by astrocyte culture. As predicted by the theory, $S(l) \propto \delta \ln l$, and the linear fit in the region between $l=2$ and $l=6$ allows the extraction of the scaling factor $\delta$. For the calculation, we used a scaling window method, described in the subsection \ref{['subsec:math_DEA']}. The same method was applied to all data sets. Final results are reported in table \ref{['tab:mu_values']}.