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Nanoscale Infrared Spectroscopy and Chemometrics Enable Detection of Intracellular Protein Distribution.

ABSTRACT: Determination of the intracellular location of proteins is one of the fundamental tasks of microbiology. Conventionally, label-based microscopy and super-resolution techniques are employed. In this work, we demonstrate a new technique that can determine intracellular protein distribution at nanometer spatial resolution. This method combines nanoscale spatial resolution chemical imaging using the photothermal-induced resonance (PTIR) technique with multivariate modeling to reveal the intracellular distribution of cell components. Here, we demonstrate its viability by imaging the distribution of major cellulases and xylanases in Trichoderma reesei using the colocation of a fluorescent label (enhanced yellow fluorescence protein, EYFP) with the target enzymes to calibrate the chemometric model. The obtained partial least squares model successfully shows the distribution of these proteins inside the cell and opens the door for further studies on protein secretion mechanisms using PTIR.

SUBMITTER: V D Dos Santos AC

PROVIDER: S-EPMC7745202 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Nanoscale Infrared Spectroscopy and Chemometrics Enable Detection of Intracellular Protein Distribution.

V D Dos Santos A Catarina AC Heydenreich Rosa R Derntl Christian C Mach-Aigner Astrid R AR Mach Robert L RL Ramer Georg G Lendl Bernhard B

Analytical chemistry 20201201 24

Determination of the intracellular location of proteins is one of the fundamental tasks of microbiology. Conventionally, label-based microscopy and super-resolution techniques are employed. In this work, we demonstrate a new technique that can determine intracellular protein distribution at nanometer spatial resolution. This method combines nanoscale spatial resolution chemical imaging using the photothermal-induced resonance (PTIR) technique with multivariate modeling to reveal the intracellula ...[more]

PMID: 33259186

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Project description:Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx. The effects of data pre-processing methods on the accuracy of the PLSR calibration models were investigated. The performance of the final model was evaluated according to the correlation coefficient (R) and root mean square error of prediction (RMSEP). For PLSR model, the best preprocessing method combination was first-order derivative, standard normal variate transformation (SNV), and mean centering, which had of 0.8805, of 0.8719, RMSEC of 0.091, and RMSEP of 0.097, respectively. The wave number variables linking to volatile oil are from 5500 to 4000 cm-1 by analyzing the loading weights and variable importance in projection (VIP) scores. For SVM model, six LVs (less than seven LVs in PLSR model) were adopted in model, and the result was better than PLSR model. The and were 0.9232 and 0.9202, respectively, with RMSEC and RMSEP of 0.084 and 0.082, respectively, which indicated that the predicted values were accurate and reliable. This work demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in M. haplocalyx.The quality of medicine directly links to clinical efficacy, thus, it is important to control the quality of Mentha haplocalyx. Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx. For SVM model, 6 LVs (less than 7 LVs in PLSR model) were adopted in model, and the result was better than PLSR model. It demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in Mentha haplocalyx. Abbreviations used: 1st der: First-order derivative; 2nd der: Second-order derivative; LOO: Leave-one-out; LVs: Latent variables; MC: Mean centering, NIR: Near-infrared; NIRS: Near infrared spectroscopy; PCR: Principal component regression, PLSR: Partial least squares regression; RBF: Radial basis function; RMSEC: Root mean square error of cross validation, RMSEC: Root mean square error of calibration; RMSEP: Root mean square error of prediction; SNV: Standard normal variate transformation; SVM: Support vector machine; VIP: Variable Importance in projection.

Dataset Information

Nanoscale Infrared Spectroscopy and Chemometrics Enable Detection of Intracellular Protein Distribution.

Publications

Nanoscale Infrared Spectroscopy and Chemometrics Enable Detection of Intracellular Protein Distribution.

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