Correction: Empirical study on the effects of acquisition parameters for FTIR hyperspectral imaging of brain tissue.

Correction for 'Empirical study on the effects of acquisition parameters for FTIR hyperspectral imaging of brain tissue' by J. Sacharz et al., Anal. Methods, 2020, 12, 4334-4342, DOI: 10.1039/C9AY01200A.

Empirical study on the effects of acquisition parameters for FTIR hyperspectral imaging of brain tissue.

Fourier transform infrared (FTIR) spectroscopic imaging is a powerful technique for molecular imaging of pathologies associated with the nervous systems including multiple sclerosis research. However, there is no standard methodology or standardized protocol for FTIR imaging of tissue sections that maximize the ability to discriminate between the molecular, white and granular layers, which is essential in the investigation of the mechanism of demyelination process. Tissue sections are heterogeneous, complex and delicate, hence the parameters to generate high quality images in minimal time becomes essential in the modern clinical laboratory. This article presents an FTIR spectroscopic imaging study of post-mortem human brain tissue testing the effects of various measurement parameters and data analysis methods on image quality and acquisition time. Hyperspectral images acquired from the same region of a tissue using a range of the most common optical and collection parameters in different combinations were compared. These included magnification (4× and 15×), number of co-added scans (1, 4, 8, 16, 32, 64 and 128 scans) and spectral resolution (4, 8 and 16 cm-1). Images were compared in terms of acquisition time, signal-to-noise (S/N) ratio, and accuracy of the discrimination between three major tissue types in a section from the cerebellum (white matter, granular and molecular layers). In the latter case, unsupervised k-means cluster (KMC) analysis was employed to generate images from the hyperspectral images, which were compared to a reference image. The classification accuracy for tissue class discrimination was highest for the 4× magnifying objective, with 4 cm-1 spectral resolution and 128 co-added scans. The 15× magnifying objective gave the best accuracy for a spectral resolution of 4 cm-1 and 64 scans (96.3%), which was just above what was achieved using the 4× magnifying objective, with 4 cm-1 spectral resolution and 32 and 64 co-added scans (95.4 and 95.6%, respectively). These findings were correlated with a decrease in S/N ratio with increasing number of scans and was generally lower for the 15× objective. However, longer scan times were required using the 15× magnifying objective, which did not justify the very small improvement in the classification of tissue types.

Detection of batch effects in liquid chromatography-mass spectrometry metabolomic data using guided principal component analysis.

Metabolomics based on liquid chromatography-mass spectrometry (LC-MS) is a powerful tool for studying dynamic responses of biological systems to different physiological or pathological conditions. Differences in the instrumental response within and between batches introduce unwanted and uncontrolled data variation that should be removed to extract useful information. This work exploits a recently developed method for the identification of batch effects in high throughput genomic data based on the calculation of a δ statistic through principal component analysis (PCA) and guided PCA. Its applicability to LC-MS metabolomic data was tested on two real examples. The first example involved the repeated analysis of 42 plasma samples and 6 blanks in three independent batches, and the second data set involved the analysis of 101 plasma and 18 blank samples in a single batch with a total runtime of 50h. The first and second data set were used to evaluate between and within-batch effects using the δ statistic, respectively. Results obtained showed the usefulness of using the δ statistic together with other approaches such as summary statistics of peak intensity distributions, PCA scores plots or the monitoring of IS peak intensities, to detect and identify instrumental instabilities in LC-MS.

Protein determination in serum and whole blood by attenuated total reflectance infrared spectroscopy.

Attenuated total reflectance mid-infrared spectra of serum and blood samples were obtained from 4,000 to 600 cm(-1). Models for the determination of albumin, immunoglobulin, total globulin, and albumin/globulin coefficients were established for serum samples, using reference data obtained by capillary electrophoresis. Based on the use of the amide bands I and II regions, the relative root mean square error of prediction (RRMSEP) was 4.9, 14.9, 4.5, and 7.1% for albumin, immunoglobulin, total globulin, and albumin/globulin coefficients, respectively, determined in an independent validation set of 120 samples using 200 samples for calibration. Additionally, the use of Kennard-Stone method for the selection of a representative calibration subset of samples provided comparable results using only 60 samples. For whole blood analysis, hemoglobin was determined in 40 validation samples using models built from 40 calibration independent samples with RRMSEP of 8.3, 5.5, and 4.9% with models built from direct spectra in the first case and from sample spectra recorded after lysis by sodium dodecyl sulfate and freezing, respectively, for the last two ones. The developed methodologies offer green alternatives for patient diagnosis in a few minutes, minimizing the use of reagents and residues and being adaptable for its use as a point-of-care method.