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Mineral-assisted thermal decomposition of formamide (HCONH2) is a heavily studied model prebiotic reaction that has offered valuable insights into the plausible pathways leading to the chemical building blocks of primordial informational polymers. To date, most efforts have focused on the analysis of formamide reaction products released in solution, although several studies have examined the role of mineral catalysts in promoting this chemistry. We show here that the direct investigation of reactive mineral surfaces by desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) gives a new perspective on the important role of the mineral surface in the formation of reaction products. As a proof-of-principle example, we show that DESI-MSI allows interrogation of the molecular products produced on heterogeneous granite samples with minimal sample preparation. Purine and pyrimidine nucleobases and their derivatives are successfully detected by DESI-MSI, with a strong correlation of the spatial product distribution with the mineral microenvironment. To our knowledge, this study is the first application of DESI-MSI to the study of complex and porous mineral surfaces and their roles in chemical evolution. This DESI-MSI approach is generally applicable to a wide range of reactions or other processes involving minerals. [ABSTRACT FROM AUTHOR]

Symbolic systems (SSs) are uniquely products of living systems, such that symbolism and life may be inextricably intertwined phenomena. Within a given SS, there is a range of symbol complexity over which signaling is functionally optimized. This range exists relative to a complex and potentially infinitely large background of latent, unused symbol space. Understanding how symbol sets sample this latent space is relevant to diverse fields including biochemistry and linguistics. We quantitatively explored the graphic complexity of two biosemiotic systems: genetically encoded amino acids (GEAAs) and written language. Molecular and graphical notions of complexity are highly correlated for GEAAs and written language. Symbol sets are generally neither minimally nor maximally complex relative to their latent spaces, but exist across an objectively definable distribution, with the GEAAs having especially low complexity. The selection pressures guiding these disparate systems are explicable by symbol production and disambiguation efficiency. These selection pressures may be universal, offer a quantifiable metric for comparison, and suggest that all life in the Universe may discover optimal symbol set complexity distributions with respect to their latent spaces. If so, the "complexity" of individual components of SSs may not be as strong a biomarker as symbol set complexity distribution. [ABSTRACT FROM AUTHOR]