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Identifying Microbial Life in Rocks: Insights from Population Morphometry

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Identifying Microbial Life in Rocks: Insights from Population Morphometry Identifying microbial life in rocks: Insights from population morphometry Joti Rouillard, Juan Manuel García-Ruiz, Linda Kah, Emmanuelle Gérard, Laurie Barrier, Sami Nabhan, Jian Gong, M. van Zuilen To cite this version: Joti Rouillard, Juan Manuel García-Ruiz, Linda Kah, Emmanuelle Gérard, Laurie Barrier, et al.. Identifying microbial life in rocks: Insights from population morphometry. Geobiology, Wiley, 2020, 18 (3), pp.282-305. 10.1111/gbi.12377. hal-03011610 HAL Id: hal-03011610 https://hal.archives-ouvertes.fr/hal-03011610 Submitted on 1 Dec 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Received: 4 July 2019 | Revised: 13 November 2019 | Accepted: 30 November 2019 DOI: 10.1111/gbi.12377 ORIGINAL ARTICLE Identifying microbial life in rocks: Insights from population morphometry Joti Rouillard1 | Juan Manuel García-Ruiz2 | Linda Kah3 | Emmanuelle Gérard1 | Laurie Barrier4 | Sami Nabhan1 | Jian Gong1 | Mark A. van Zuilen1 1Equipe Géomicrobiologie, Université de Paris, Institut de Physique du Globe de Paris, Abstract CNRS, Paris, France The identification of cellular life in the rock record is problematic, since microbial 2 Laboratorio de Estudios Cristalográficos, life forms, and particularly bacteria, lack sufficient morphologic complexity to be ef- Instituto Andaluz de Ciencias de la Tierra, Consejo Superior de Investígacìones fectively distinguished from certain abiogenic features in rocks. Examples include Cientificas–Universidad de Granada, organic pore-fillings, hydrocarbon-containing fluid inclusions, organic coatings on Granada, Spain 3Department of Earth and Planetary exfoliated crystals and biomimetic mineral aggregates (biomorphs). This has led to Sciences, University of Tennessee, Knoxville, the interpretation and re-interpretation of individual microstructures in the rock re- TN, USA cord. The morphologic description of entire populations of microstructures, how- 4Equipe Tectonique et Mécanique de la Lithosphère, Université de Paris, Institut de ever, may provide support for distinguishing between preserved micro-organisms Physique du Globe de Paris, CNRS, Paris, and abiogenic objects. Here, we present a statistical approach based on quantita- France tive morphological description of populations of microstructures. Images of modern Correspondence microbial populations were compared to images of two relevant types of abiogenic Joti Rouillard, Equipe Géomicrobiologie, Institut de Physique du Globe de Paris, 1 rue microstructures: interstitial spaces and silica–carbonate biomorphs. For the popula- Jussieu, 75238 Paris cedex 5, France. tions of these three systems, the size, circularity, and solidity of individual particles Email: [email protected] were calculated. Subsequently, the mean/SD, skewness, and kurtosis of the statisti- Funding information cal distributions of these parameters were established. This allowed the qualitative European Research Council (ERC) under the European Union's Horizon 2020 Research and quantitative comparison of distributions in these three systems. In addition, the and Innovation Program, Grant/Award fractal dimension and lacunarity of the populations were determined. In total, 11 Number: 646894; ERC Seventh Framework Programme FP7/2007-2013, Grant/Award parameters, independent of absolute size or shape, were used to characterize each Number: 340863; Ministerio de Economía population of microstructures. Using discriminant analysis with parameter subsets, y Competitividad of Spain, Grant/Award Number: CGL2016-78971-P it was found that size and shape distributions are typically sufficient to discriminate populations of biologic and abiogenic microstructures. Analysis of ancient, yet unam- biguously biologic, samples (1.0 Ga Angmaat Formation, Baffin Island, Canada) sug- gests that taphonomic effects can alter morphometric characteristics and complicate image analysis; therefore, a wider range of microfossil assemblages should be studied in the future before automated analyses can be developed. In general, however, it is clear from our results that there is great potential for morphometric descriptions of populations in the context of life recognition in rocks, either on Earth or on extrater- restrial bodies. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2019 The Authors. Geobiology published by John Wiley & Sons Ltd Geobiology. 2019;00:1–24. wileyonlinelibrary.com/journal/gbi | 1 2 | ROUILLARD ET al. 1 | INTRODUCTION indigenous character of a carbonaceous microfossil, enabling the exclusion of modern organic contaminants and modern endoliths. The use of morphology as a tool for identification of microfossils, We extend here the morphologic approach to entire popula- especially in the most ancient records, has been fraught with diffi- tions of microstructures. This will specifically bring more light and culties. Early life forms lacked sufficient morphologic complexity to precision to one of the aforementioned criteria of biogenicity: the be easily distinguished from abiogenic structures, such as pore-fill- association of individual structures representing a biologic popula- ings, interstitial spaces between crystals, air bubbles, and fluid in- tion. A critical question to address is whether populations of abio- clusions. Furthermore, certain abiotic self-organized structures, genic structures such as interstitial spaces and biomorphic mineral including silica–carbonate biomorphs (Carnerup, 2007; García-Ruiz aggregates can be quantitatively distinguished from populations of et al., 2003; Rouillard, García-Ruiz, Gong, & Zuilen, 2018), chemi- micro-organisms. If it can be shown that this is possible, preliminary cal gardens (McMahon, 1916), carbon–sulfur biomorphs (Cosmidis tests of biogenicity can be made without the use of complex and & Templeton, 2016), or manganese oxide biomorphs (Muscente, expensive in situ analytical techniques. Czaja, Tuggle, Winkler, & Xiao, 2018) also have the potential to cre- One possible approach to discriminate between different ate a variety of shapes, including spheroids, framboids, helicoids, types of populations of microstructures is by using artificial in- or filaments that resemble individual cells or clusters of cells. The telligence. Automated classification has been made possible with shapes adopted by microbial cells and colonies are therefore not the use of multivariate analyses and has improved with the de- unique to living systems. Besides, the interpretation of ancient sam- velopment of machine learning (Bishop, 2006; Domingos, 2012; ples must consider that post-depositional geologic processes, that McLachlan, 2004; Pedregosa et al., 2011; Robert, 2014; Snoek, could have introduced secondary abiogenic structures, may affect Larochelle, & Adams, 2012). This technique has been applied in a the morphology of fossilized life. As a consequence, the identifica- variety of fields, including igneous petrology (facies recognition; tion of ancient microfossils oftentimes remains controversial. The Młynarczuk, Górszczyk, & Ślipek, 2013), entomology (taxon rec- most well-known controversy concerns the occurrence of filamen- ognition; MacLeod, Benfield, & Culverhouse, 2010), palynology tous microstructures in the 3.5 Ga Apex Chert. Interpreted initially (taxon recognition; Holt, Allen, Hodgson, Marsland, & Flenley, as filamentous micro-organisms (Schopf, 1993; Schopf, Kitajima, 2011), and forensic science (face and fingerprint recognition; Spicuzza, Kudryavtsev, & Valley, 2018; Schopf & Kudryavtsev, Etemad & Chellappa, 1997; Farina, Kovacs-Vajna, & Leone, 1999). 2009, 2012; Schopf, Kudryavtsev, Agresti, Wdowiak, & Czaja, In theory, using an assemblage of example images for biogenic 2002; Schopf, Kudryavtsev, Sugitani, & Walter, 2010) preserved in and biomimicking populations, machine learning could be used to sedimentary chert, these structures have been subsequently rein- empirically distinguish populations of micro-organisms from abio- terpreted as infilling in the microporous structure of recrystallized genic populations. However, machine learning approaches usually silica (Brasier et al., 2002, 2005, 2011), as exfoliated mica crystals necessitate a large number of images, which may be difficult to (Wacey, Saunders, Kong, Brasier, & Brasier, 2015) in a hydrothermal obtain for the populations of relevance here. Additionally, many chert vein, and have been compared to silica–carbonate biomorphs machine learning algorithms make it difficult to discretely identify known to precipitate within silica-rich, alkaline media (García-Ruiz the decisive criteria that distinguish the systems. et al., 2003). Another approach constitutes the quantitative description of the In order to answer these difficulties, specific criteria have been morphology (or morphometry) of entire populations of microstruc- proposed to assess the biologic origin of potential microfossils in tures. The shapes and sizes of individual
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