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Standardization of complex biologically derived spectrochemical datasets

Abstract

Spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy are used to study interactions of light with biological materials. This interaction forms the basis of many analytical assays used in disease screening/diagnosis, microbiological studies, and forensic/environmental investigations. Advantages of spectrochemical analysis are its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, an urgent need exists for repetition and validation of these methods in large-scale studies and across different research groups, which would bring the method closer to clinical and/or industrial implementation. For this to succeed, it is important to understand and reduce the effect of random spectral alterations caused by inter-individual, inter-instrument and/or inter-laboratory variations, such as variations in air humidity and CO2 levels, and aging of instrument parts. Thus, it is evident that spectral standardization is critical to the widespread adoption of these spectrochemical technologies. By using calibration transfer procedures, in which the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variation can be normalized into a single model using computational-based methods, such as direct standardization (DS) and piecewise direct standardization (PDS); therefore, measurements performed under different conditions can generate the same result, eliminating the need for a full recalibration. Here, we have constructed a protocol for model standardization using different transfer technologies described for FTIR spectrochemical applications. This is a critical step toward the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random variations are present.

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Fig. 1: IR spectra of healthy control (absence of disease) samples created by varied ATR-FTIR instruments and operators.
Fig. 2: PCA scores for healthy control (absence of disease) samples created by varied ATR-FTIR instruments before and after standardization.
Fig. 3: Flowchart for standardization using direct standardization (DS).
Fig. 4
Fig. 5: Discriminant function (DF) plots using PCA-LDA to discriminate healthy control (absence of disease) samples from ovarian cancer samples with various instruments.
Fig. 6: PCA-LDA results for DS and PDS standardization models for spectra collected by the three different instruments.
Fig. 7: Discriminant function (DF) plots using PCA-LDA to discriminate healthy control (absence of disease) samples from ovarian cancer samples with varying operators.
Fig. 8: PCA-LDA results for DS and PDS standardization models for spectra collected by two different operators.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding authors on reasonable request.

Software availability

Outlier detection algorithm: https://doi.org/10.6084/m9.figshare.7066613.v1

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Acknowledgements

C.L.M.M. thanks Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Brazil (grant 88881.128982/2016-01) for financial support. The work in the laboratory of F.L.M. was supported in part by The Engineering and Physical Sciences Research Council (EPSRC; grant nos: EP/K023349/1 and EP/K023373/1). M.P. acknowledges the Rosemere Cancer Foundation for funding.

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F.L.M. is the principal investigator who conceived and developed the idea for the article; C.L.M.M. and M.P. wrote the manuscript. L.C., N.J.F., M.I., K.M.G.L., P.L.M.-H., H.S., J.T., M.J.W., D.Z. and Y.-G.Z. contributed recommendations and provided feedback and changes to the manuscript, and C.L.M.M., M.P. and F.L.M. brought together the text and finalized the manuscript.

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Correspondence to Camilo L. M. Morais, Maria Paraskevaidi or Francis L. Martin.

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Journal peer review information: Nature Protocols thanks Åsmund Rinnan and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

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Baker, M. J. et al. Nat. Protoc. 9, 1771–1791 (2014): https://doi.org/10.1038/nprot.2014.110

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Integrated supplementary information

Supplementary Figure 1 IR spectra of the same type of samples measured by different ATR-FIR spectrometers at the same institution.

ad, Average (a) raw and (b) preprocessed spectra for healthy control samples, and average (c) raw and (d) preprocessed spectra for cancer samples across three different instruments (A, B and C).

Supplementary Figure 2 PCA scores for preprocessed spectra acquired by different ATR-FIR spectrometers at the same institution and outlier detection test.

a, PCA scores for healthy control samples according to the instrument used for spectra acquisition (A, B and C). b, PCA scores for cancer samples according to the instrument used for spectra acquisition (A, B and C). c, Hotelling’s T2 versus Q residuals test for healthy control samples according to the instrument used for spectra acquisition (A, B and C) based on a PCA using 5 PCs (94.77% cumulative variance). d, Hotelling’s T2 versus Q residuals test for cancer samples according to the instrument used for spectra acquisition (A, B and C) based on a PCA using 5 PCs (92.96% cumulative variance). Circled samples in c and d indicate outliers removed. Confidence ellipse was 95%, depicted in blue in a and b.

Supplementary Figure 3 PCA loadings for preprocessed spectra acquired by different ATR-FIR spectrometers at the same institution.

a, PCA loadings for healthy control samples measured in different instruments (A, B and C). b, PCA loadings for cancer samples measured in different instruments (A, B and C).

Supplementary Figure 4 IR spectra of healthy control samples measured by different operators at the same institution.

a,b, Average (a) raw and (b) pre-processed spectra for healthy control samples acquired with instrument A depending on the operator. c,d, Average (c) raw and (d) preprocessed spectra for healthy control samples acquired with instrument B depending on the operator. e,f, Average (e) raw and (f) preprocessed spectra for healthy control samples acquired with instrument C, varying the operator.

Supplementary Figure 5 IR spectra of ovarian cancer samples measured by different operators at the same institution.

a,b, Average (a) raw and (b) preprocessed spectra for cancer samples acquired with instrument A depending on the operator. c,d, Average (c) raw and (d) preprocessed spectra for cancer samples acquired with instrument B depending on the operator. e,f, Average (e) raw and (f) preprocessed spectra for cancer samples acquired with instrument C depending on the operator.

Supplementary Figure 6 PCA scores for preprocessed spectra acquired by different operators at the same institution.

a,b, PCA scores for (a) healthy control and (b) cancer samples acquired with instrument A depending on the operator. c,d, PCA scores for (c) healthy control and (d) cancer samples acquired with instrument B depending on the operator. e,f, PCA scores for (e) healthy control and (f) cancer samples acquired with instrument C depending on the operator. Confidence ellipse was 95%, depicted in blue.

Supplementary Figure 7 Outlier detection test for healthy controls and ovarian cancer samples.

a, Hotelling’s T2 versus Q residuals test based on a PCA using 8 PCs (99.07% cumulative variance) for healthy control samples depending on the instrument for spectra acquisition (A, B and C) used by operator 2. b, Hotelling’s T2 versus Q residuals test based on a PCA using 5 PCs (96.92% cumulative variance) for cancer samples depending on the instrument for spectra acquisition (A, B and C) used by operator 2. Circled sample in a indicates an outlier removed. The Hotelling’s T2 versus Q residuals test for operator 1 is depicted in Supplementary Fig. 2c,d.

Supplementary Figure 8 PCA scores for healthy controls (HC) and ovarian cancer (OC) samples based on the spectra acquired by both operators (1 and 2) and by all instruments (A, B and C).

Confidence ellipse at a 95% confidence level is depicted in blue.

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Morais, C.L.M., Paraskevaidi, M., Cui, L. et al. Standardization of complex biologically derived spectrochemical datasets. Nat Protoc 14, 1546–1577 (2019). https://doi.org/10.1038/s41596-019-0150-x

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