Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Magnussen EA

PROVIDER: S-EPMC9814771 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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

Magnussen Eirik Almklov EA Zimmermann Boris B Blazhko Uladzislau U Dzurendova Simona S Dupuy-Galet Benjamin B Byrtusova Dana D Muthreich Florian F Tafintseva Valeria V Liland Kristian Hovde KH Tøndel Kristin K Shapaval Volha V Kohler Achim A

Communications chemistry 20221222 1

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 inf ...[more]

PMID: 36697906

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Project description:The James Webb Space Telescope (JWST) will deliver an unprecedented quantity of high-quality spectral data over the 0.6-28 μm range. It will combine sensitivity, spectral resolution, and spatial resolution. Specific tools are required to provide efficient scientific analysis of such large data sets. Our aim is to illustrate the potential of unsupervised learning methods to get insights into chemical variations in the populations that carry the aromatic infrared bands (AIBs), more specifically polycyclic aromatic hydrocarbon (PAH) species and carbonaceous very small grains (VSGs). We present a method based on linear fitting and blind signal separation for extracting representative spectra for a spectral data set. The method is fast and robust, which ensures its applicability to JWST spectral cubes. We tested this method on a sample of ISO-SWS data, which resemble most closely the JWST spectra in terms of spectral resolution and coverage. Four representative spectra were extracted. Their main characteristics appear consistent with previous studies with populations dominated by cationic PAHs, neutral PAHs, evaporating VSGs, and large ionized PAHs, known as the PAH x population. In addition, the 3 μm range, which is considered here for the first time in a blind signal separation (BSS) method, reveals the presence of aliphatics connected to neutral PAHs. Each representative spectrum is found to carry second-order spectral signatures (e.g., small bands), which are connected with the underlying chemical diversity of populations. However, the precise attribution of theses signatures remains limited by the combined small size and heterogeneity of the sample of astronomical spectra available in this study. The upcoming JWST data will allow us to overcome this limitation. The large data sets of hyperspectral images provided by JWST analysed with the proposed method, which is fast and robust, will open promising perspectives for our understanding of the chemical evolution of the AIB carriers.

Dataset Information

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

Publications

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

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