Global biochemical profiling of fast-growing Antarctic bacteria isolated from meltwater ponds by high-throughput FTIR spectroscopy.

Fourier transform infrared (FTIR) spectroscopy is a biophysical technique used for non-destructive biochemical profiling of biological samples. It can provide comprehensive information about the total cellular biochemical profile of microbial cells. In this study, FTIR spectroscopy was used to perform biochemical characterization of twenty-nine bacterial strains isolated from the Antarctic meltwater ponds. The bacteria were grown on two forms of brain heart infusion (BHI) medium: agar at six different temperatures (4, 10, 18, 25, 30, and 37°C) and on broth at 18°C. Multivariate data analysis approaches such as principal component analysis (PCA) and correlation analysis were used to study the difference in biochemical profiles induced by the cultivation conditions. The observed results indicated a strong correlation between FTIR spectra and the phylogenetic relationships among the studied bacteria. The most accurate taxonomy-aligned clustering was achieved with bacteria cultivated on agar. Cultivation on two forms of BHI medium provided biochemically different bacterial biomass. The impact of temperature on the total cellular biochemical profile of the studied bacteria was species-specific, however, similarly for all bacteria, lipid spectral region was the least affected while polysaccharide region was the most affected by different temperatures. The biggest temperature-triggered changes of the cell chemistry were detected for bacteria with a wide temperature tolerance such Pseudomonas lundensis strains and Acinetobacter lwoffii BIM B-1558.

Explorative characterization and taxonomy-aligned comparison of alterations in lipids and other biomolecules in Antarctic bacteria grown at different temperatures.

Temperature significantly impacts bacterial physiology, metabolism and cell chemistry. In this study, we analysed lipids and the total cellular biochemical profile of 74 fast-growing Antarctic bacteria grown at different temperatures. Fatty acid diversity and temperature-induced alterations aligned with bacterial classification-Gram-groups, phylum, genus and species. Total lipid content, varied from 4% to 19% of cell dry weight, was genus- and species-specific. Most bacteria increased lipid content at lower temperatures. The effect of temperature on the profile was complex and more species-specific, while some common for all bacteria responses were recorded. Gram-negative bacteria adjusted unsaturation and acyl chain length. Gram-positive bacteria adjusted methyl branching (anteiso-/iso-), chain length and unsaturation. Fourier transform infrared spectroscopy analysis revealed Gram-, genus- and species-specific changes in the total cellular biochemical profile triggered by temperature fluctuations. The most significant temperature-related alterations detected on all taxonomy levels were recorded for mixed region 1500-900 cm-1 , specifically the band at 1083 cm-1 related to phosphodiester groups mainly from phospholipids (for Gram-negative bacteria) and teichoic/lipoteichoic acids (for Gram-positive bacteria). Some changes in protein region were detected for a few genera, while the lipid region remained relatively stable despite the temperature fluctuations.

Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra.

Infrared spectroscopy delivers abundant information about the chemical composition, as well as the structural and optical properties of intact samples in a non-destructive manner. We present a deep convolutional neural network which exploits all of this information and solves full-wave inverse scattering problems and thereby obtains the 3D optical, structural and chemical properties from infrared spectroscopic measurements of intact micro-samples. The proposed model encodes scatter-distorted infrared spectra and infers the distribution of the complex refractive index function of concentrically spherical samples, such as many biological cells. The approach delivers simultaneously the molecular absorption, sample morphology and effective refractive index in both the cell wall and interior from a single measured spectrum. The model is trained on simulated scatter-distorted spectra, where absorption in the distinct layers is simulated and the scatter-distorted spectra are estimated by analytic solutions of Maxwell's equations for samples of different sizes. This allows for essentially real-time deep learning-enabled infrared diffraction micro-tomography, for a large subset of biological cells.

Deep convolutional neural network recovers pure absorbance spectra from highly scatter-distorted spectra of cells.

Infrared spectroscopy of cells and tissues is prone to Mie scattering distortions, which grossly obscure the relevant chemical signals. The state-of-the-art Mie extinction extended multiplicative signal correction (ME-EMSC) algorithm is a powerful tool for the recovery of pure absorbance spectra from highly scatter-distorted spectra. However, the algorithm is computationally expensive and the correction of large infrared imaging datasets requires weeks of computations. In this paper, we present a deep convolutional descattering autoencoder (DSAE) which was trained on a set of ME-EMSC corrected infrared spectra and which can massively reduce the computation time for scatter correction. Since the raw spectra showed large variability in chemical features, different reference spectra matching the chemical signals of the spectra were used to initialize the ME-EMSC algorithm, which is beneficial for the quality of the correction and the speed of the algorithm. One DSAE was trained on the spectra, which were corrected with different reference spectra and validated on independent test data. The DSAE outperformed the ME-EMSC correction in terms of speed, robustness, and noise levels. We confirm that the same chemical information is contained in the DSAE corrected spectra as in the spectra corrected with ME-EMSC.