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Extraction of useful information from unstructured, large and complex mass spectrometric signals is a challenge in many application fields of mass spectrometry. Therefore, new data analysis approaches are required to help uncover the complexity of such signals. In this contribution, we examined the chemical composition of the 1.88 Ga Gunflint chert using the newly developed high mass resolution laser ionization mass spectrometer (fs-LIMS-GT). We report results on the following: 1) mass-spectrometric multi-element imaging of the Gunflint chert sample; and 2) identification of multiple chemical entities from spatial mass spectrometric data utilizing nonlinear dimensionality reduction and spectral similarity networks. The analysis of 40 ' 000 mass spectra reveals the presence of chemical heterogeneity (seven minor compounds) and two large clusters of spectra registered from the organic material and inorganic host mineral. Our results show the utility of fs-LIMS imaging in combination with manifold learning methods in studying chemically diverse samples.

The investigation of chemical composition on planetary bodies without significant sample processing is of importance for nearly every mission aimed at robotic exploration. Moreover, it is a necessary tool to achieve the longstanding goal of finding evidence of life beyond Earth, for example, possibly preserved microbial remains within martian sediments. Our Laser Ablation Ionization Mass Spectrometer (LIMS) is a compact time-of-flight mass spectrometer intended to investigate the elemental, isotope, and molecular composition of a wide range of solid samples, including e.g., low bulk density organic remains in microfossils. Here, we present an overview of the instrument and collected chemical spectrometric data at the micrometer level from a Precambrian chert sample (1.88 Ga Gunflint Formation, Ontario, Canada), which is considered to be a martian analogue. Data were collected from two distinct zones-a silicified host area and a carbon-bearing microfossil assemblage zone. We performed these measurements using an ultrafast pulsed laser system (pulse width of similar to 180 fs) with multiple wavelengths (infrared [IR]-775 nm, ultraviolet [UV]-387 nm, UV-258 nm) and using a pulsed high voltage on the mass spectrometer to reveal small organic signals. We investigated (1) the chemical composition of the sample and (2) the different laser wavelengths' performance to provide chemical depth profiles in silicified media. Our key findings are as follows: (1) microfossils from the Gunflint chert reveal a distinct chemical composition compared with the host mineralogy (we report the identification of 24 elements in the microfossils); (2) detection of the pristine composition of microfossils and co-occurring fine chemistry (rare earth elements) requires utilization of the depth profiling measurement protocol; and (3) our results show that, for analysis of heterogeneous material from siliciclastic deposits, siliceous sinters, and cherts, the most suitable wavelength for laser abla

In this contribution, we investigated the chemical composition of Precambrian microfossils from the Gunflint chert (1.88 Ga) using a miniature laser ablation ionization mass spectrometer (LIMS) developed for in situ space applications. Spatially resolved mass spectrometric imaging (MSI) and depth profiling resulted in the acquisition of 68,500 mass spectra. Using single mass unit spectral decomposition and multivariate data analysis techniques, we identified the location of aggregations of microfossils and surrounding inorganic host mineral. Our results show that microfossils have unique chemical compositions that can be distinguished from the inorganic chert with high fidelity. Chemical depth profiling results also show that with LIMS microprobe data, it is possible to identify chemical differences between individual microfossils, thereby providing new insights about nature of early life. Analysis of LIMS spectra acquired from the individual microfossils reveals complex mineralization, which can reflect the metabolic diversity of the Gunflint microbiome. An intensity-based machine learning model trained on LIMS Gunflint data might be applied for the future investigations of putative microfossils from silicified matrices, where morphological integrity of investigated structures is lost, and potentially in the investigation of rocks acquired from the Martian surface., Note in: Journal of Chemometrics, vol. 37, issue 9, article number e3517DOI: 10.1002/cem.3517