Cluster-Partial Least Squares (c-PLS) regression analysis: Application to miRNA and metabolomic data

BACKGROUND

Biomedicine and biological research frequently involve analyzing large datasets generated by high-throughput technologies like genomics, transcriptomics, miRNomics, and metabolomics. Pathway analysis is a common computational approach used to understand the impact of experimental conditions, phenotypes, or interventions on biological pathways and networks. This involves statistical analysis of omic data to identify differentially expressed variables and mapping them onto predefined pathways. Analyzing such datasets often requires multivariate techniques to extract meaningful insights such as Partial Least Squares (PLS). Variable selection strategies like interval-PLS (iPLS) help improve understanding and predictive performance by identifying informative variables or intervals. However, iPLS is suboptimal to treat omic data such as metabolic or miRNA profiles, where features cannot be distributed along a continuous dimension describing their relationships as in e.g., vibrational or nuclear magnetic resonance spectroscopy.

RESULTS

This study introduces a novel variable selection approach called cluster PLS (c-PLS) that aims to assess the joint impact of variable groups selected based on biological characteristics (such as miRNA-regulated metabolic pathway or lipid classes) on the predictive performance of a multivariate model. The usefulness of c-PLS is shown using miRNomic and metabolomic datasets obtained from the analysis of 24 liver tissue biopsies collected in the frame of a clinical study of steatosis.

SIGNIFICANCE AND NOVELTY

Results obtained show that c-PLS enables analyzing the effect of biologically relevant variable clusters, facilitating the identification of biological processes associated with the independent variable, and the prioritization of the biological factors influencing model performance, thereby improving the understanding of the biological factors driving model predictions. While the strategy is tested for the evaluation of PLS models, it could be extended to other linear and non-linear multivariate models.

Comparing the direct assessment of steatosis in liver explants with mid- and near-infrared vibrational spectroscopy, prior to organ transplantation

A fast and accurate assessment of liver steatosis is crucial during liver transplantation surgery as it can negatively impact its success. Recent research has shown that near-infrared (NIR) and attenuated total reflectance-Fourier transform mid-infrared (ATR-FTIR) spectroscopy could be used as real-time quantitative tools to assess steatosis during abdominal surgery. Here, in the frame of a clinical study, we explore the performance of NIR and ATR-FTIR spectroscopy for the direct assessment of steatosis in liver tissues. Results show that both NIR and ATR-FTIR spectroscopy are able to quantify the % of steatosis with cross-validation errors of 1.4 and 1.6%, respectively. Furthermore, the two portable instruments used both provided results within seconds and can be placed inside an operating room evidencing the potential of IR spectroscopy for initial characterization of grafts in liver transplantation surgery. We also evaluated the complementarity of the spectral ranges through correlation spectroscopy.

Enhancing the accuracy of mid-infrared spectroscopy-based liver steatosis quantification using digital image analysis as a reference

The assessment of liver steatosis is crucial in both hepatology and liver transplantation (LT) surgery. Steatosis can negatively impact the success of LT. Steatosis is a factor for excluding donated organs for LT, but the increasing demand for transplantable organs has led to the use of organs from marginal donors. The current standard for evaluating steatosis is a semi-quantitative grading based on the visual examination of a hematoxylin and eosin (H&E)-stained liver biopsy, but this method is time-consuming, subjective, and lacks reproducibility. Recent research has shown that infrared (IR) spectroscopy could be used as a real-time quantitative tool to assess steatosis during abdominal surgery. However, the development of IR-based methods has been hindered by the lack of appropriate quantitative reference values. In this study, we developed and validated digital image analysis methods for the quantitation of steatosis in H&E-stained liver sections using univariate and multivariate strategies including linear discriminant analysis (LDA), quadratic DA, logistic regression, partial least squares-DA (PLS-DA), and support vector machines. The analysis of 37 tissue samples with varying grades of steatosis demonstrates that digital image analysis provides accurate and reproducible reference values that improve the performance of IR spectroscopic models for steatosis quantification. A PLS model in the 1810-1052 cm^-1 region using first derivative ATR-FTIR spectra provided RMSECV = 0.99%. The gained improvement in accuracy critically enhances the applicability of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) to support an objective graft evaluation at the operation room, which might be especially relevant in cases of marginal liver donors to avoid unnecessary graft explantation.

Metabolomic Diversity of Human Milk Cells over the Course of Lactation-A Preliminary Study

Human milk (HM) is a complex biofluid containing a wide cell variety including epithelial cells and leukocytes. However, the cellular compositions and their phenotypic properties over the course of lactation are poorly understood. The aim of this preliminary study was to characterize the cellular metabolome of HM over the course of lactation. Cells were isolated via centrifugation and the cellular fraction was characterized via cytomorphology and immunocytochemical staining. Cell metabolites were extracted and analyzed using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-QqTOF-MS) in the positive and negative electrospray ionization modes. Immunocytochemical analysis revealed a high variability of the number of detected cells with relative median abundances of 98% of glandular epithelial cells, 1% of leukocytes, and 1% of keratinocytes. Significant correlations between the milk postnatal age with percentage of epithelial cells and leukocytes, and with total cell count were observed. Results from the Hierarchical Cluster Analysis of immunocytochemical profiles were very similar to those observed in the analysis of the metabolomic profiles. In addition, metabolic pathway analysis showed alterations in seven metabolic pathways correlating with postnatal age. This work paves the way for future investigations on changes in the metabolomic fraction of the cellular compartment of HM.

Combining Pharmacokinetics and Vibrational Spectroscopy: MCR-ALS Hard-and-Soft Modelling of Drug Uptake In Vitro Using Tailored Kinetic Constraints

Raman microspectroscopy is a label-free technique which is very suited for the investigation of pharmacokinetics of cellular uptake, mechanisms of interaction, and efficacies of drugs in vitro. However, the complexity of the spectra makes the identification of spectral patterns associated with the drug and subsequent cellular responses difficult. Indeed, multivariate methods that relate spectral features to the inoculation time do not normally take into account the kinetics involved, and important theoretical information which could assist in the elucidation of the relevant spectral signatures is excluded. Here, we propose the integration of kinetic equations in the modelling of drug uptake and subsequent cellular responses using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and tailored kinetic constraints, based on a system of ordinary differential equations. Advantages of and challenges to the methodology were evaluated using simulated Raman spectral data sets and real Raman spectra acquired from A549 and Calu-1 human lung cells inoculated with doxorubicin, in vitro. The results suggest a dependency of the outcome on the system of equations used, and the importance of the temporal resolution of the data set to enable the use of complex equations. Nevertheless, the use of tailored kinetic constraints during MCR-ALS allowed a more comprehensive modelling of the system, enabling the elucidation of not only the time-dependent concentration profiles and spectral features of the drug binding and cellular responses, but also an accurate computation of the kinetic constants.

ATR-Spin: an open-source 3D printed device for direct cytocentrifugation onto attenuated total reflectance crystals

Infrared spectroscopy (IR) enables the direct and rapid characterization of cells at the molecular level. Achieving a rapid and consistent cell preparation is critical for the development of point-of-care diagnostics for cell analysis. Here we introduce an open-source, 3D printed device for integrating the isolation, preconcentration, and measurement of attenuated total reflectance IR spectra of cells from biofluids. The tool comprises a disposable card for cytocentrifugation, equipped with magnets, which allows reproducible integration into the pathlength of the IR spectrophotometer. Preliminary results using cell culture media containing A549 cells indicate that this system enables a qualitative and quantitative characterization of cells down to 10 cells μL^-1 by using a single and cost-effective device and within a few minutes.

Correction: Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells

Correction for 'Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells' by Jitraporn Vongsvivut et al., Analyst, 2019, 144, 3226-3238, DOI: 10.1039/C8AN01543K.

Toward Rapid Screening of Liver Grafts at the Operating Room Using Mid-infrared Spectroscopy

The estimation of steatosis in a liver graft is mandatory prior to liver transplantation, as the risk of graft failure increases with the level of infiltrated fat. However, the assessment of liver steatosis before transplantation is typically based on a qualitative or semiquantitative characterization by visual inspection and palpation and histological analysis. Thus, there is an unmet need for transplantation surgeons to have access to a diagnostic tool enabling an in situ fast classification of grafts prior to extraction. In this study, we have assessed an attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopic method compatible with the requirements of an operation room for the evaluation of the lipid contents in human livers. A set of 20 human liver biopsies obtained from organs intended for transplantation were analyzed by expert pathologists, ATR-FTIR spectroscopy, lipid biochemical analysis, and UPLC-ESI(+/-)TOFMS for lipidomic profiling. Comparative analysis of multisource data showed strong correlations between ATR-FTIR, clinical, and lipidomic information. Results show that ATR-FTIR captures a global picture of the lipid composition of the liver, along with information for the quantification of the triradylglycerol content in liver biopsies. Although the methodology performance needs to be further validated, results support the applicability of ATR-FTIR for the in situ determination of the grade of liver steatosis at the operation room as a fast, quantitative method, as an alternative to the qualitative and subjective pathological examination.

Discriminant analysis and feature selection in mass spectrometry imaging using constrained repeated random sampling - Cross validation (CORRS-CV)

The identification of biomarkers through Mass spectrometry imaging (MSI) is gaining popularity in the clinical field. However, considering the complexity of spectral and spatial variables faced, data mining of the hyperspectral images can be troublesome. The discovery of markers generally depends on the creation of classification models which should be validated to ensure the statistical significance of the discriminants m/z detected. Internal validation using resampling methods such as cross validation (CV) are widely used for model selection, the estimation of its generalization performance and biomarker discovery when sample sizes are limited and an independent test set is not available. Here, we introduce for first time the use of Constrained Repeated Random Subsampling CV (CORRS-CV) on multi-images for the validation of classification models on MSI. Although several aspects must be taken into account (e.g. image size, CORRS-CV∂value, the similarity across spatially close pixels, the total computation time), CORRS-CV provides more accurate estimates of the model performance than k-fold CV using of biological replicates to define the data split when the number of biological replicates is scarce and holding images back for testing is a waste of valuable information. Besides, the combined use of CORRS-CV and rank products increases the robustness of the selection of discriminant features as candidate biomarkers which is an important issue due to the increased biological, environmental and technical variabilities when analysing multiple images, especially from human tissues collected in clinical studies.

Whole-Organism Analysis by Vibrational Spectroscopy

Vibrational spectroscopy has contributed to the understanding of biological materials for many years. As the technology has advanced, the technique has been brought to bear on the analysis of whole organisms. Here, we discuss advanced and recently developed infrared and Raman spectroscopic instrumentation to whole-organism analysis. We highlight many of the recent contributions made in this relatively new area of spectroscopy, particularly addressing organisms associated with disease with emphasis on diagnosis and treatment. The application of vibrational spectroscopic techniques to entire organisms is still in its infancy, but new developments in imaging and chemometric processing will likely expand in the field in the near future.

Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells

Coupling synchrotron IR beam to an ATR element enhances spatial resolution suited for high-resolution single cell analysis in biology, medicine and environmental science.

Analysis of multi-source metabolomic data using joint and individual variation explained (JIVE)

Metabolic profiling is increasingly being used for understanding biological processes but there is no single analytical technique that provides a complete quantitative or qualitative profiling of the metabolome. Data fusion (i.e. joint analysis of data from multiple sources) has the potential to circumvent this issue facilitating knowledge discovery and reliable biomarker identification. Another field of application of data fusion is the simultaneous analysis of metabolomic changes through several biofluids or tissues. However, metabolomics typically deals with large datasets, with hundreds to thousands of variables and the identification of shared and individual factors or structures across multiple sources is challenging due to the high variable to sample ratios and differences in intensity and noise range. In this work we apply a recent method, Joint and Individual Variation Explained (JIVE), for the integrated unsupervised analysis of metabolomic profiles from multiple data sources. This method separates the shared patterns among data sources (i.e. the joint structure) from the individual structure of each data source that is unrelated to the joint structure. Two examples are described to show the applicability of JIVE for the simultaneous analysis of multi-source data using: (i) plasma samples subjected to different analytical techniques, sample treatment and measurement conditions; and (ii) plasma and urine samples subjected to liquid chromatography-mass spectrometry measured using two ionization conditions.

Application of Discriminant Analysis and Cross-Validation on Proteomics Data

High-throughput proteomic experiments have raised the importance and complexity of bioinformatic analysis to extract useful information from raw data. Discriminant analysis is frequently used to identify differences among test groups of individuals or to describe combinations of discriminant variables. However, even in relatively large studies, the number of detected variables typically largely exceeds the number of samples and the classifiers should be thoroughly validated to assess their performance for new samples. Cross-validation is a widely approach when an external validation set is not available. In this chapter, different approaches for cross-validation are presented including relevant aspects that should be taken into account to avoid overly optimistic results and the assessment of the statistical significance of cross-validated figures of merit.

Assessment of the statistical significance of classifications in infrared spectroscopy based diagnostic models

Fourier transform infrared (IR) spectroscopy in combination with multivariate data analysis is a versatile tool that can be applied to disease diagnosis. However, a rigorous validation of the obtained models is necessary in order to obtain robust results. This work evaluates the advantages of the use of permutation testing for determining the statistical significance of the misclassification errors obtained from IR based diagnostic models through cross validation (CV). The model performance, estimated by CV, is compared to a distribution of CV-performance values obtained using randomly permuted class labels. The distribution of 'random CV-values' is considered as a null distribution and used to establish the significance of the model estimators obtained using real class labels. ATR-FTIR spectra of serum samples were classified using random forest (RF) classifiers according to two criteria, the tag number (a randomly assigned pseudo class membership) and the level of urea (real class). CV errors obtained were compared to the null distribution of CV errors from a permutation test and an independent validation set. The procedure was evaluated testing typical conditions leading to overoptimistic estimations provided by the CV like e.g. the size of subsamples used during CV, variable selection and the use of replicates. Results show that for the tag number (pseudo class), CV indicated classification errors between 23 and 33% depending on the subsample size employed. Those values were even lower when variable selection or replicates were used. However, permutation testing indicated that those CV errors were non-significant. In contrast, for sample classification according to their levels of urea, all cross validation errors were found to be significant. Although the proposed method is computationally intensive, it provides a simple way of calculating an empirical p-value of the CV-estimator, thus establishing the statistical significance and providing a feasibility indicator especially useful for studies where the number of samples is limited.

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.

Synthesis and biological evaluation of combretastatin A-4 and three combretastatin-based hybrids

The syntheses of combretastatin A-4 from gallic acid and of three combretastatin-based hybrids are described. Starting from commercial gallic acid, the phosphonium salt (3,4,5-trimethoxybenzylphosphonium bromide) is synthesized and coupled, through a Wittig reaction, with several aldehydes, including methoxymethyl-protected isovanillin, the aldehyde y-bicyclohomofarnesal having a labdane skeleton, 3-(3-pyridyl) propanal, and furfural. The biological properties of the cis-coupled compounds as cytotoxic, antiviral and antifungal agents are also reported. In addition, pyrogallol, gallic and 3,4,5-trimethoxybenzoic acids have been studied biologically.

Synthesis and Biological Evaluation of Combretastatin A-4 and Three Combretastatin-Based Hybrids

The syntheses of combretastatin A-4 from gallic acid and of three combretastatin-based hybrids are described. Starting from commercial gallic acid, the phosphonium salt (3,4,5-trimethoxybenzylphosphonium bromide) is synthesized and coupled, through a Wittig reaction, with several aldehydes, including methoxymethyl-protected isovanillin, the aldehyde γ-bicyclohomofarnesal having a labdane skeleton, 3-(3-pyridyl) propanal, and furfural. The biological properties of the cis-coupled compounds as cytotoxic, antiviral and antifungal agents are also reported. In addition, pyrogallol, gallic and 3,4,5-trimethoxybenzoic acids have been studied biologically.