Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers . In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes. The metabolome is a collection of small molecules <1500 Da) or metabolites that are involved in cellular processes, including energy production, signaling, and synthesis of cellular components. It integrates information from the genome and regulatory processes, as well as environmental factors such as diet and lifestyle. Because the metabolome reflects the downstream effects of these factors on cellular function, it is very close to the actual phenotype of a cell, tissue, or organism. As such, studying the metabolome can provide valuable insights into disease mechanisms, biomarker discovery, drug development, and precision medicine 500 Da or.The most employed analytical techniques in metabolomics are gas chromatography (GC) and liquid chromatography (LC) coupled to mass spectrometry (MS), both enabling the simultaneous assessment of many metabolites in large cohorts ,4. DespiPre-analytical variation is introduced by different collection containers, pre-storage preparation, and sample storage conditions. For example, metabolic profiles of blood plasma can be affected by different types of anticoagulant-coated tubes ,6 as welThe occurrence rate of these unexpected RT shift events increases as the number of analyzed samples increases.For this task, several alignment algorithms and computer programs are publicly and commercially available, but due to the high complexity of the metabolome, further improvements of existing approaches as well as manual interventions are still needed ,24,25. WAnother challenge for large cohorts are significant variations of feature intensities due to instrument drifts; these effects are usually categorized into intra- and inter-batch effects. In this regard, a batch is defined as a set of samples processed and analyzed by the same experimental procedure (same operator and instrument) in an uninterrupted manner. Since the capacity of certain chromatographic equipment  is limited, cohorts with a higher number of samples are typically partitioned into several batches . Intra- In this review, we outline the importance of incorporating QC samples into the measurement sequence. The review then compares three different methods for adjusting batch effects using QC samples. The first method is a simple and easy-to-implement median-based normalization technique , the secThe application of intrastudy QC samples has been recognized as a valuable tool to significantly improve the validity of large-scale metabolomics studies ,35,40,41There are three major reasons to employ QC samples: The first is the initial equilibration of the measurement system. Each sequence of samples should start with conditioning QC samples. In the case of GC-MS and LC-MS, usually the data of the first four to eight injections are not stable ,48,49. TThe second reason to employ QC samples is the evaluation of measurement precision. As all QCs are equal in terms of metabolite concentration and sample matrix, quantitative quality criteria such as the relative standard deviation (RSD) and Dispersion-ratio (D-ratio) can be determined for quality assessment.The third and most important reason is the modeling and correction of systemic error. Since QC samples are measured intermittently throughout the whole sequence run, changes in instrument performance can be accurately monitored. Data of QCs quantitatively reveal gradual changes in instrument sensitivity, which is extremely useful for the elimination of batch variations. For this, it is crucial to include a sufficient number of QCs into the sequence to maximize the performance of batch-effect correction and lower the risk of overfitting . On the Besides just detecting gradual changes in instrument sensitivity, QC samples are also used for the correction of these effects. In general, such a correction is performed as follows: As batch effects are metabolite specific, each recorded metabolite level needs to be analyzed and corrected, separately. The QC recordings for each metabolite define a pattern of instrument-related signal changes as a function of the injection order. Because all biological samples are flanked by corresponding QCs, it can be assumed that instrument-related signal alterations in the QCs also apply to the neighboring biological samples. This is the reason why a mathematical model can be employed to predict batch variation based on the information of the QCs. The predicted batch variability is then subtracted from the original data to yield batch-effect-free data. A qualified QC-based correction does not only account for inter- and intra-batch effects but is also resistant to overfitting. This is important in order to deter the model from accounting for random variations in the data, leading to overoptimistic quality measures, but non-usable data. In the following, we review and compare three often applied correction methods in regard to their effectiveness for batch-effect removal.i is calculated based on these three QC samples  is an advanced regression-based method . For thiEquation ). In conf minimizes the distance between model fit and QCs under consideration of a roughness penalty, controlled by the smoothing parameter p  models with different hyperparameter combinations, chosen from a hyperparameter pool defined by the user. The Random Forest model is trained on data comprised of error ratios X. The error ratio is calculated as follows: y denotes the raw signal of the objective metabolite and y across the whole data set. The model’s performance is evaluated in a K-fold cross validation with a loss function of kth CV fold. Based on the loss function, the base model will receive a weight, such that high-performing models have high weights and under-performing models have low weights, but its information is still considered.n is the number of all base models, and i is the ith base model. For the actual data correction, the base models are retrained on the whole data set. The error ratios X are used to train the RF model. Hence, the RF model will also predict error ratios, which need to be converted back to metabolite signals. The final result of the algorithm is the weighted sums of all base models  is the most sophisticated algorithm for the batch-effect correction discussed here. It is an adaptable ensemble learning architecture comprised of several base models . The TIGe models C.By selecting only a small set of features and the RF algorithm, TIGER’s base models are of moderate complexity, thus mitigating the risk of capturing random noise in addition to the technical variation. Moreover, an ensemble learning architecture is employed, further improving the models’ robustness and lowering the risk of overfitting by considering the output from strong as well as weak models. Altogether, the TIGER algorithm prioritizes robustness and high generalization over high complexity, making this a valuable method even for data with small sample sizes or data with a high degree of noise.i within the QCs by dividing the standard deviation To evaluate the performance of the different batch-effect correction methods, we employed three different quantitative quality criteria, namely the RSD and D-ratio for each metabolite, as well as the Euclidean distance of QCs after the principal component analysis (PCA). The RSD is a widely used metric, which is calculated for each metabolite A typically accepted RSD threshold for metabolites in biomarker discovery should be below 20% for LC-MS and below 30% for GC-MS ,44,47,48ariation .(6)D-raA D-ratio close to 0% would be a perfect measurement, where the technical variance is zero and all observed variance originates from the biological variation. On the other hand, a D-ratio of 100% would be the worst possible measurement, where there is no biological variation and only noise is detected. A metabolite, where referred .Another method to visually evaluate batch-effect-removal is the PCA. By plotting the first two principal components, the clustering of batches and the removal of those can be observed. High quality data show tightly clustered QC data points at the origin of the PCA and equally distributed sample points around the QCs. The Euclidean distance between the centroid of the QCs, and each QC sample point can be calculated to quantitatively assess the effect of the batch-effect correction on the QCs itself.Here, iability . These tTo provide recommendations on which method to choose, we evaluated all of the three above highlighted batch-effect correction methods and performed RSD, D-ratio, and a PCA. Furthermore, the methods were applied to normalize GC-MS and LC-MS data sets to evaluate differences in measurement techniques.All batch-effect correction methods reduced the technical error in each tested data set, as demonstrated by the reduced RSD as compared to the raw data A . Here,To further evaluate the different methods, we performed a PCA. As described in the previous chapter, we expected all QC samples to cluster tightly together after removing the batch effects, while we expected all biological samples to be distributed across the plot. The PCA of the raw data clearly partitioned the QCs into the underlying batches for every data set, with GC-MS 1 having the most substantial batch effects. After batch-effect correction, all methods resulted in similar plots with tightly clustered QC samples at the origin of the PCA plot scores and equally distributed biological samples. Therefore, all methods were able to eliminate batch effects regardless of the analytical technique and led to a small analytical variation relative to the biological variation. To quantitatively analyze the PCA plots, we calculated the Euclidean distance between each QC sample point and its corresponding centroid . The EucHowever, it is important to note, that the previously described evaluation criteria are all based on the same QCs used to fit and train the batch-effect correction methods. This could lead to overoptimistic results; furthermore, it does not measure the impact of the batch-effect correction on the biological samples. For this purpose, different machine learning classifiers were trained to classify persons into Parkinson’s disease or healthy control groups based on a published cerebrospinal fluid metabolomics data set to test the power of these algorithms in terms of biological information . Before Next, we selected the metabolic features to train the model on using recursive feature elimination (RFE). This method trains the model on the full number of features and assigns each feature an importance metric. The least important features are removed, and the process is repeated until a pre-defined number of features will be reached. Here, ten features were selected for each corrected data set by RFE, resulting in three sets of features. Citramalate, pyroglutamate, tryptophan, urea, and glycine were present in all three feature sets and, finally, were used to train the classification models. Here, we focus on three popular probabilistic classifiers, namely boosted Logistic Regression (LogitBoost), Random Forest (RF), and radial kernel Support Vector Machine . For the optimal evaluation of the classifiers, we performed a double repeated cross-validation (CV) approach with two loops. The outer loop generates 100 random splits of training and hold-out sets. The inner loop is used to tune the models’ hyperparameters on the training data by maximizing the Area under the Receiver Operating Characteristic curve (AUROC) with repeated CV . The model with the highest AUROC in the CV then predicts the patients’ class of the hold-out set. The results of the predictions is shown in In summary, TIGER performed best for the batch-effect correction in our evaluation reflected in the lowest RSD, D-ratio, and median Euclidean distance of QC samples in the PCA. This was also the reason why many more metabolites passed our predefined quality criteria and remained in the data set for further analysis. In addition, the performance of machine learning classifiers trained on the data set corrected with TIGER demonstrated the best performance, emphasizing that this batch-effect correction method captures the inter-phenotype information present in the data in the most optimal way. All in all, we recommend using TIGER as the preferred method for the batch effect correction due to the minor loss of metabolic information. On the other hand, QC-RSC is in the advantage for the normalization of very large cohorts, as the computation costs of TIGER are relatively high.A prerequisite for the successful application of the above discussed batch-effect correction algorithms is an accurate chromatogram alignment and picking of quantification peaks. The correct alignment of metabolite features across measurements is crucial for an accurate quantification in the context of every metabolomics’ analysis. RT shifts of molecular features within- and between batches can result in wrong alignments of metabolite features, especially for closely eluting metabolites with similar or even identical mass spectra, such as isomers. In the following, we present a strategy to screen and to correct for quantification errors due to metabolite feature misalignments. We take advantage of the fact that EI ionization generates highly reproducible fragmentation patterns for a certain metabolite. We assume that an increased or decreased metabolite amount in the sample affects all fragment ions equally, and that the same applies for a potential drop in instrument sensitivity over time. Hence, the ratio of a pair of fragment ion intensities must be identical for a certain metabolite over all measurements and independent of the sample or instrument condition. As a demo data set, we chose a previously published GC-MS-based metabolomics data set that was recorded for CSF samples in the context of Parkinson’s disease .Within this data set, gluconic acid elutes at 25.65 min and the peak integrals of fragment ions 205, 305, and 333 were automatically assigned for quantification A. HoweveIn this article, we reviewed and evaluated three popular algorithms suited for the elimination of mass spectrometric noise based on intermittently measured QC samples in metabolomics studies with a high number of samples. Furthermore, we introduced a strategy to improve chromatogram alignment and peak picking in GC-MS data. To increase the quality of large metabolomics studies, an optimal workflow should include the following steps: 1. Preparation of adequate QCs is essential for bigger cohorts. 2. The sequence run should start with five to ten QC samples solely for equilibration of the analytical system. 3. The sequence of sample measurements should be random and at least one QC sample should be measured in-between three to seven samples. In addition there should be two QC samples at the sequence beginning and end. 4. For high sample numbers, the sample blocks between QCs should be increased to avoid an unnecessary extension in analysis time. For lower sample numbers, the sequence should contain at least eight QCs (without the conditioning QCs). After data acquisition, the peak picking, integration and mapping of metabolites should be verified. For this, we propose an easy-to-implement method to check the quantification of metabolites based on log-fold changes of the quantification ions’ intensities. To advise which method to employ for the batch effect removal, we evaluated three different methods: a median-based approach, QC-RSC, and TIGER. Although all three methods significantly removed batch effects and drifts in instrument sensitivity, TIGER always outperformed the other two methods. For this reason and despite the high computational cost of this algorithm, we advise using TIGER. Many more metabolites passed the quality criteria (RSD < 30% and D-ratio < 50%), and overall separation between tested phenotypes was more evident, as highlighted in the better performance of employed machine learning classifiers.Untargeted metabolomics is an important tool for biomarker discovery, drug development, and precision medicine. These fields rely heavily on large data sets to provide the needed statistical power. Here, the machine learning approach with an ensemble learning architecture has been proven to be the most promising tool for batch-effect correction. Therefore, the performance of batch-effect correction could be further improved by applying deep learning approaches. Furthermore, this could enable the comparison of untargeted metabolomic studies between instruments, which opens the possibility of mining large databases across studies. Potentially, this could lead to the identification of biological mechanisms or biomarkers that would otherwise be hidden in smaller individual studies.


Mycobacterium tuberculosis complex (MTC), important not only for the livestock sector but also for public health due to its zoonotic character. Despite the numerous efforts that have been carried out to improve the performance of the current antemortem diagnostic procedures, nowadays, they still pose several drawbacks, such as moderate to low sensitivity, highlighting the necessity to develop alternative and innovative tools to complement control and surveillance frameworks. Volatilome analysis is considered an innovative approach which has been widely employed in animal science, including animal health field and diagnosis, due to the useful and interesting information provided by volatile metabolites. Therefore, this study assesses the potential of gas chromatography coupled to ion mobility spectrometry (GC-IMS) to discriminate cattle naturally infected (field infections) by MTC from non-infected animals. Volatile organic compounds (VOCs) produced from feces were analyzed, employing the subsequent information through chemometrics. After the evaluation of variable importance for the projection of compounds, the final discriminant models achieved a robust performance in cross-validation, as well as high percentages of correct classification (>90%) and optimal data of sensitivity (91.66%) and specificity (99.99%) in external validation. The tentative identification of some VOCs revealed some coincidences with previous studies, although potential new compounds associated with the discrimination of infected and non-infected subjects were also addressed. These results provide strong evidence that a volatilome analysis of feces through GC-IMS coupled to chemometrics could become a valuable methodology to discriminate the infection by MTC in cattle.Bovine tuberculosis is considered a re-emerging disease caused by different species from the Mycobacterium tuberculosis complex) in cattle. The good robust performance of discriminant models as well as the optimal data of sensitivity and specificity achieved highlight volatilome analysis as an innovative approach with huge potential.Bovine tuberculosis is endemic in many countries worldwide and poses important concerns for public health because of their zoonotic condition. However, current diagnostic techniques present several hurdles, such as low sensitivity and complexity, among others. In this regard, the development of new approaches to improve the diagnosis and control of this disease is considered crucial. Volatile organic compounds are small molecular mass metabolites which compose volatilome, whose analysis has been widely employed with success in different areas of animal science including animal health. The present study seeks to evaluate the combination of fecal volatilome analysis with chemometrics to detect field infections by bovine tuberculosis ( Mycobacterium tuberculosis complex (MTC) constitutes a wide group of slow-growing mycobacteria species where mainly Mycobacterium bovis (and others such as Mycobacterium caprae) stands out as the etiological agent for bovine tuberculosis (bTB) , especially in developing countries with significant livestock bTB prevalence (is (bTB) , 2, as wis (bTB) . bTB is is (bTB) , 5. Althevalence –8. Apaevalence . This dievalence .Although major efforts have been made to develop control and surveillance programs in order to eradicate bTB, these are frequently considered inadequate or insufficient. In this regard, some drawbacks associated with current diagnostic techniques and their poor performance, together with other epidemiological and geographical aspects, have been pointed out as responsible for the difficulty in achieving the objective of disease control, especially in endemic countries , 11. Thiin vivo assays available have been identified. Regarding the infection of M. bovis in cattle, there are only a scarce number of studies published in which experimental infections and controlled conditions prevail, and the exhaled breath is the main sample evaluated (Volatilome is defined as the group of volatile organic compounds (VOCs) produced by a specific sample or organism, a term framed under volatilomics . These svaluated .in vivo assays of this VOC analysis application is considered important. In recent years, the combination of gas chromatography coupled to ion mobility spectrometry (GC-IMS) has made a breakthrough in the field of metabolomics due to its advantages of high SE, convenient operation, and low cost. In this regard, the goal of the present study was to evaluate and validate the combination of the GC-IMS technique and chemometrics to discriminate naturally infected cattle by MTC from non-infected animals. For this purpose, feces have been selected as the sample of choice because of the interesting information which can be conveyed from the individual physiology  using an automatized BD Bacter MGIT System (Becton Dickinson).Briefly, the microbiological culture consisted of a decontamination process of the tissue homogenates with an equal volume of 0.75% (wt/vol: 1/1) hexadecyl pyridinium chloride solution in agitation for 30 min . This wa6110-forward: 5′-GGTAGCAGACCTCACCTATGTGT-3′; IS6110-reverse: 5′-AGGCGTCGGTGACAAAGG-3′) and probe (IS6110-probe: 5′-FAM-CACGTAGGCGAACCC-MGBNFQ-3′) were used targeting IS6110  was employed to evaluate each sample in duplicate in the MyiQ2 Two-Color real-time PCR Detection System . A detection limit ranging from 10 to 100 genomic equivalents as well as a cut-off set at Cq <38 were previously described  silicone septum.A FlavourSpec instrument  equipped with an autosampler  was employed for volatilome evaluation. Briefly, the samples inside glass headspace vials were incubated at 60°C for 15 min with agitation at 500 r.p.m. After incubation, 100 µL of the headspace gas phase of the vial was automatically injected into the injector under splitless mode with the syringe of the autosampler, both at 80°C. Chromatographic separation was performed at 45°C on a HP-5 capillary column 30 m × 0.32 mm ID × 0.5 µm film thickness . Nitrogen (99.999% purity) was used as the carrier gas under the following programmed flow: 1 mL/min for 13 min, 15 mL/min for 2 min, 20 mL/min for 13 min, after which the flow stopped. The analytes were ionized in the ion mobility spectrometry (IMS) ionization chamber, where a radiation energy of 6.5 KeV and 9.8  cm of drift tube were maintained at 75°C. The drift gas (nitrogen gas) was set at 150 mL/min. Every spectrum was recorded as the average scan of 12.The integrated LAV (version 2.2.1) and VOCal (version 0.2.9) software were used for viewing and processing measurement data acquired by the instrument. The n-ketones C4–C9  were employed as external references to calculate the retention index (RI) of VOCs under the same chromatographic conditions as the samples. Then, VOCs were identified by comparing the RI and drift time of standards in the GC-IMS Library .Considering the magnitude of GC-IMS data, the resulting information after feces analysis was treated by means of chemometrics. Specifically, a targeted approach was selected to process volatilome information. The preparation of data for its processing was as follows: the topographic maps generated by GC-IMS equipment were individually examined to select those available signals or markers. Afterwards, LAV software was employed to convert raw IMS data to .csv format, obtaining each individual signal (or feature) intensity. This step allowed data use and processing by MATLAB software  and its PLS Toolbox extension  .Subsequently, a principal component analysis (PCA) was initially performed in order to reduce data dimensionality and thus explore data, visualize potential trends and detect possible outliers. Data were preprocessed by autoscaling  previously to modeling. Classification models were based on the application of partial least squares-discriminant analysis (PLS-DA). For the calibration and cross-validation  of the models, the data set was split into a training set of 75% of the samples  and a test set with the remaining 25% , which was used for the subsequent external validation (as blind samples). Considering the limited number of samples available for the present study, the sample set was randomly split three times, each with the validation and calibration percentages previously mentioned, to improve model robustness and accuracy. Moreover, the different splits between training and test sets were always done by keeping the ratio of samples as in the original set and balancing the representation of each group. The final number of latent variables selected in PLS-DA models was assessed by the minimum root mean square classification error in CV and prediction.The variable importance for the projection (VIP) of every feature initially selected was also obtained to reduce data complexity and refine the final discriminant model, attempting to improve discrimination success by eliminating those features with a limited influence on model performance  and keeping those considered significant . VIP evaluation and the subsequent feature reduction were successively performed several times. Thus, sequential PLS-DA classification models were developed by selecting in each one the features with VIP value of ≥1, due to the VIPs being scaled in such a way that all compounds that have a VIP of ≥1 are considered relevant . TherefoThe diagnostic performance of the present approach was evaluated by comparing with the microbiological culture. However, since the culture is considered an imperfect reference technique for MTC diagnosis , the staP value from t-test to visually identify those significant features based on their statistical significance as well as to select potential biomarkers of the two groups in comparison. This representation was performed with the autoscaled intensity from all the VOCs detected after GC-IMS analysis.Lastly, MetaboAnalyst (version 5.0) software was used to perform a volcano plot statistical analysis. This analysis combined the results from fold change analysis and As mentioned above, VOC signals in the topographic plots were tentatively identified according to their retention and drift time using VOCal software and the available GC-IMS library .x and y axes, respectively) and the intensity, attending to the signals’ color and size. GC-IMS analysis of fecal samples resulted in two-dimensional-topographic plots where the different VOCs detected are characterized by the drift and retention times  were used to classify samples of the corresponding three validation sets  as blind samples . The confusion matrix as well as results related to the classification and diagnostic performance of the external validation and CV obtained in models are available in Concerning external validation results obtained with the initial PLS-DA models, only one sample from MTC-infected cattle (belonging to Set 2) was incorrectly classified as non-infected. Hence, an average percentage of classification error of 8.33% was obtained for this group. As with the infected group, only one sample from the group of negative animals (belonging to Set 3) was incorrectly classified inside the group of infected cattle, reaching an 11.11% mean classification error in this group. The correct category classification percentage of each set of samples is also collected in Despite the good classification and diagnostic performance obtained for external validation of discriminant models, the results of CV were quite different. In general, lower percentages of correct classification, SE and SP, were reached, ranging from 55% to 77%. Against this background, which reflects a low model robustness, VIP evaluation was subsequently performed in order to refine PLS-DA models and improve CV and external validation.The score plots of the three PLS-DA models performed with Sets 1, 2, and 3 are available in VIP values of the 260 features employed for the PLS-DA model development were obtained for the three sets of samples. Subsequently, several reductions of features were applied considering the criteria explained in the methodology subsection : for Set 1, four reductions of features were applied, obtaining seven features  as the definitive number for discriminant model development (the remaining 253 features were removed). In the case of Sets 2 and 3, this variable reduction process was repeated three times, deleting 243 and 250 features, respectively. The final numbers of features employed for PLS-DA model development in these sets were 17 for Set 2  and 10 for Set 3 . In general, the reduction of features after VIP evaluation resulted in a greater performance of classification models developed with Sets 1, 2, and 3 of samples. The results obtained for CV and external validation of the final PLS-DA models are summarized in P value and fold change result of this group of features. Furthermore, some of these above-mentioned 23 compounds were identified based on the searching results of the GC-IMS Library: dimer of acetic acid methyl ester (VOC 22), propanoic acid (VOC 47), 3-methylbutanal (VOC 119), 3-methyl-1-pentanol (VOC 158), pentenal (VOC 175), methyltrimethoxysilane (VOC 180), ethyl 2-methylpropanoate (VOC 190), thiophene (VOC 191), and hexanal (VOC 220). The information about this identification is also included in In general, the final number of VOCs selected after VIP evaluation for the three sets of samples was 23 (some of them were common to the three sets). t-test of all the 260 VOCs initially obtained. This type of representation enabled discerning, after quick observation, those VOCs which obtained the greatest changes and were also significant. Three out of the 260 VOCs represented in the volcano plot  were significant for the group of MTC-infected animals, while 23 features  were associated with negative animals (In addition to the VIP evaluation, the importance of some of these VOCs was confirmed by another statistic test, the volcano plot, which highlighted the relevance of 26 VOCs detected after GC-IMS analysis of feces . It was  animals . The VOCP value, and results of fold change analysis from the 26 relevant VOCs above-mentioned is summarized in The identification performed for the group of relevant compounds shown by volcano plot is also available in VOCs can be directly analyzed from diverse biological matrices such as blood, serum, breath, feces, sweat, skin, and urine. However, cattle feces were chosen in the present study for several reasons. First, it is considered one of the most valuable sources of information about the physiology and metabolic state of an individual . Thus, aThe visual differences highlighted in the present study between the two types of samples in the evaluation of the topographic plots were consistent with the subsequent results of discriminant models, which achieved high percentages of correct classification. The variations in terms of signal intensity as well as the absence or presence of several features would have enabled obtaining a successful differentiation of infected and non-infected animals. Information about 260 features was finally obtained due to the SE of GC-IMS, which has been underlined as an important advantage compared with other analytical techniques used for volatilome evaluation since low-concentration volatile compounds can be clearly detected , 32. TheAlthough this methodology is not yet a widespread combination, it offers some advantages compared to other technologies: apart from the SE mentioned above, GC-IMS is operated under atmospheric pressure, making the adaptation of compact and portable options possible . This woThe evaluation of VIP performed in the present study for the three sets of samples enabled substantially reducing data complexity but also improving both the classification and diagnostic performance of discriminant models. These findings are in line with the extent of use given to VIP in the literature, as it has been shown to be useful for the selection of those features which are most relevant in terms of discrimination. The features or variables with a VIP value higher than 1 are considered to be significant for differentiation, while those whose VIP value is below 1 can be removed because of its scarce relevance . In thisA scarce number of studies have been previously focused on the analysis of VOCs and its association with bTB infection in cows. Three studies have used exhaled breath as a source of volatilome information –39; twP values obtained in Some differences in terms of number and intensity of VOCs between infected and non-infected samples were visually observed , obtainiThe VOCs listed in In contrast, eight of the potential markers currently identified have not previously been associated with the discrimination of the infection by MTC in animals: propanoic acid, pentan-1-ol, pentenal, methyltrimethoxysilane, 3-methyl-1-pentanol, and 3-methylbutanal in the case of non-infected animals and propanal and ethyl 2-methylpropanoate as relevant for MTC-infected cows. This last group of two novel VOCs might be directly related to the infection by MTC as evaluated herein, but the first group is also considered important due to the possibility of VOC production, reduction, and inhibition associated with the infection. While the information already published by other authors comes mainly from studies with experimentally infected cattle, the present results were obtained after the evaluation of animals naturally infected with MTC. Therefore, the study of a natural infection may have revealed some differences and new VOCs associated with the process in comparison with assays conducted in controlled conditions. Our results underline the importance of validating data obtained from experimental infections in settings under field conditions.in vivo assays which attempt to optimize this methodology is considered very scarce (M. bovis-inoculated animals (The interest of these results is also connected with the context of volatilome analysis as an innovative approach for the detection of bTB infection, where the number of y scarce . The onl animals . InteresIn addition to feces, other samples such as exhaled breath or serum have been previously employed for bTB infection detection through volatilome evaluation in cattle . HoweverThe present study is consistent with the methodology previously employed in investigations within the framework of volatilome analysis as a diagnostic methodology for mycobacteria infection. The number of animals included in this type of assay is usually small due to the complexity of the sampling methodology, the diagnostic reference necessarily demanded, or the need for specific facilities and equipment. In fact, the only study which has previously evaluated fecal volatilome from cattle to discriminate bTB infection included only 20 calves in the experimental design . Furtherantemortem diagnostic methodology for the detection of field infections by MTC, as well as to evaluate the repeatability of the group of new features highlighted. Furthermore, considering that the collection of feces consists of an easy and simple process which offers several advantages in comparison with other biological matrices, this study showed that it could be addressed as the sample of choice in future approximations of volatilome analysis, not only with cattle but also with other animal species.The present study has shown important differences in the VOCs produced in the headspace over fecal samples from MTC-infected and non-infected cattle. The combination between GC-IMS analysis and chemometric data treatment has been useful to obtain a high percentage of correct classification in discriminant models, optimal SE and SP parameters in the diagnostic performance, as well as a significative reduction in VOCs needed for the discrimination. Some of these compounds have also been pointed out as relevant after VIP evaluation and volcano plot for the discrimination between infected and non-infected animals. Our results also reveal some new VOCs which could be associated with the course of MTC infection. Against that background, future studies which increase the sample size would be of great benefit to confirm the potential of this approach as an


Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to high dimensionality and diverse nature of data, and noise associated with each platform. Sparsity in data, non-overlapping features and technical batch effects make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards due to their simplistic nature with less capacity. In addition, existing methods for single cell multi-omics integration are computationally expensive. Therefore, in this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions. It has a light-weight architecture with substantially reduced number of parameters. The proposed model is capable of learning a latent low-dimensional embedding that can extract useful features from the data facilitating further downstream analyses. UMINT has been applied to integrate healthy and disease CITE-seq (paired RNA and surface proteins) datasets including a rare disease Mucosa-Associated Lymphoid Tissue (MALT) tumor. It has been benchmarked against existing state-of-the-art methods for single cell multi-omics integration. Furthermore, UMINT is capable of integrating paired single cell gene expression and ATAC-seq (Transposase-Accessible Chromatin) assays as well. Recent advancements in single cell technologies have provided unprecedented opportunities in analysis of omics data. This allows researchers to probe biological functions at the cellular level while studying embryonic development, immune system or cancer  and REAPAt present, several methods exist that can perform the task of integration of single cell omics modalities. Seuratv3 ), cumulatively affect quite a substantial percentage of patients. Overall, there are more than 7,000 variants of RDs. RDs affect patients’ and their families’ quality of life, and have significant societal impact. Due to the rarity of each RD, it is extremely difficult to properly diagnose and treat these individuals, as well as engage them into research to upgrade therapies. With the advancements in omics technologies, molecular understanding of RDs has improved over time leading to their rapid diagnosis. To combine multi-omics data from various technologies, Artificial Intelligence (AI)-based integration techniques are, nevertheless, becoming more and more necessary. Deep learning (DL) methodologies to integrate and query data from several heterogeneous sources may also be utilised to dramatically accelerate the discovery of efficient RD therapies . UMINT is competent enough to integrate different single cell omics layers of high dimensions with ease. It produces a latent low-dimensional embedding that can extract relevant features from the data, which facilitate further analyses. It can also reconstruct the data with high accuracy. Further, UMINT does not make assumptions about the distribution of data, and can integrate a variable number of omics modalities. In addition, UMINT owns a light-weight architecture and is thus computationally far less expensive than some of the existing unsupervised neural network based methods (like those based on autoencoder networks), used for single cell multi-omics integration. The performance of UMINT has been demonstrated on multiple publicly available healthy and disease datasets. These comprise four CITE-seq datasets, one of which contains cells from MALT tumor, a rare variant of malignant lymphoma. We have benchmarked the results against several existing state-of-the-art algorithms used for single cell multi-omics integration, which fall under different categories. In-silico experimental results compare favourably for UMINT against these state-of-the-art methods. Additionally, the performance of UMINT has been validated through integration of an auxiliary multimodal single cell paired gene expression and ATAC-seq (chromatin accessibility) data. Finally, as a further extension to this work, UMINT has been used to integrate bulk multi-omics data with more than two omics layers, which it has been able to execute with ease. Thus, UMINT’s ability to integrate widely heterogenous omics data  with varying number of omics layers boosts its utility as a powerful integration model and makes it completely fit in with the in silico experimental results obtained on different datasets used in this work and provides a detailed comparison against other existing methods for single cell multi-omics integration. In The remaining part of this article has been organized as follows. This section describes the datasets used in the experiments, the methodology used for data pre-processing and the proposed neural network model, called UMINT, for single cell multi-omics integration. cbmc8k . It has been preprocessed via MUON . UMINT is a non-recurrent feed-forward neural network that is efficient enough to integrate variable number of omics layers and extract a latent embedding at a reduced dimension. The network structure of UMINT represents a novel neural network architecture as shown in X1, X2, …, Xm be m datasets corresponding to m different omics modalities having n samples (cells) each with d1, d2, …, dm features  respectively. The UMINT architecture consists of two sub-architectures - an encoder and a decoder. The encoder accepts data from multi-omics datasets presented to the Input layer, transports them through one or more Modality encoding layer(s) and integrates them in the final layer, known as the Bottleneck layer. The decoder accepts the embedded output from the Bottleneck layer, transports them through one or more Modality decoding layers and finally tries to reconstruct the original data at the Reconstruction layer. In this work, we have used only one layer each for modality encoding and decoding. However, UMINT may contain multiple such layers based on the requirement. In order to improve generalization capability and reduce dimension of the latent embedding, the number of neurons at the Bottleneck layer has been kept smaller than the number of neurons in the Input layer.Let Input layer of UMINT consists of m different modules, each of which accepts input from a data modality. The number of neurons in each of the modules in the Input layer is equal to the dimensions of the individual data modalities d1, d2, …, dm respectively. In this layer, UMINT tries to find a suitable projection for each of the data modalities that may be good enough to get integrated in subsequent layers. Each module in the first Modality encoding layer shares a dense connection with the corresponding modules of the Input layer. The first Modality encoding layer containing m modules thus accepts data from m modules in the Input layer as input, and obtains m different projections. Let aji and hji be the input to and the output from the ith module in the jth layer respectively. Then, for the Input and Modality encoding layers, we havexi is a sample in ith modality, and ReLU(y) = max. The term Wi1 denotes the weights between ith module of the Input layer and ith module of the first Modality encoding layer, and bi1 denotes the bias terms of the nodes in ithModality encoding layer. Finally, the outputs from the Modality encoding layer(s) are projected onto a lower dimensional space in the Bottleneck layer. The final Modality encoding layer and the Bottleneck layer are fully connected. If Wi2 represents the weights between the ith module of the final Modality encoding layer and the Bottleneck layer, then we haveb21 denotes the bias terms of the nodes in Bottleneck layer. This concludes the process of encoding. The overall function of the encoder network can thus be represented asThe Bottleneck layer is projected onto the Modality decoding layer(s) which consists of the same number of modules as that in the Modality encoding layer(s). The number of neurons in each module of the Modality decoding layer is identical to that used in the modules in the Modality encoding layer. The last layer of the decoder is the Reconstruction layer which tries to reconstruct the original data from the respective modules in the final Modality decoding layer. The process of decoding can be expressed asWji and bji represent weights and biases for the ith module in the jth layer respectively. Thus, the decoder function is given byxi.Reconstruction of the original data is done by the decoder network in exactly the opposite manner to that of encoding. The integrated embedding coming out of the Input Layer of UMINT. For each cell x, UMINT tries to find an optimal reconstruction λi. Additionally, in order to limit over-fitting, we have used an L1 regularization on the nodes’ activities to allow sparsity of nodes’ outputs and an L2 regularization on the weight values since L2 regularization tries to shift weight values towards zero. Both L1 and L2 regularizations minimize the model complexity. In this work, L1 and L2 regularizations have been controlled using regularization parameters α and β respectively. The objective function thus becomesIn this scope of work, UMINT has been initially used to integrate scRNA-seq and single cell protein expression data. Subsequently, it has been used to integrate scRNA-seq and ATAC-seq data. Thus, for each dataset, paired RNA and ADT assays, or paired RNA and ATAC assays form the inputs to different modules of the α and β have been set to 0.0001 and 0.001 respectively, as recommended in literature  and Fowlkes Mallows Index (FMI) scores, to measure the degree of agreement between the actual and predicted cell-types, for all the methods used in comparison including UMINT. The actual cell types corresponding to the ground truth data have been obtained from the corresponding source datasets mentioned in C = [cij], where cij indicates the total number of points belonging to both ith cluster of the first set and jth cluster of the second set. ARI is an external cluster validity index, and is thus defined aspi = ∑jcij, qj = ∑icij and N = ∑ijcij respectively. An ARI value close to 1 indicates good resemblance between two clusters. Similarly, FMI, another external evaluation index used to measure the similarity between two sets of cluster labels, is defined asTP, FP and FN denotes the count of True Positives, False Positives and False Negatives respectively. The FMI score lies between 0 and 1, and a high value implies a good similarity between two clusters.For two sets of cluster labels, the overlap between them is represented by a contingency table At a subsequent stage of the work, UMINT has been evaluated on another multiome dataset containing paired gene expresssion and ATAC-seq data. After preprocessing each modality and reducing them to highly variable features, UMINT has been used to extract a latent 64-dimensional embedding by integrating the RNA and ATAC assays. This latent embedding has been further subjected to agglomerative hierarchical clustering and k-means clustering. The embedding quality has been assessed using external evaluation criteria like, ARI and FMI, as explained above.cbmc8k, MALT10k and bmcite30k, where cbmc8k and bmcite30k contain cells from healthy samples, and MALT10k contains cells from rare lymphoma (MALT). The latent embedding produced by UMINT has been first compared with that produced by Autoencoder (AE)-based architectures. Subsequently, the UMINT-generated embedding has been compared with three other state-of-the-art single cell multi-omics integration methods. kotliarov50k having multiple batches. For evaluating the batch correction performance of UMINT, we have performed two different experiments on kotliarov50k dataset, with and without batch integration. Finally, as an extended utility, UMINT has been used to integrate paired gene expression and ATAC-seq data. The integrated embedding generated by UMINT has then been validated through clustering techniques. Additionally, UMINT has been validated on a bulk multi-omics cancer dataset for integration and classification.UMINT has been applied on a variety of datasets containing cells from both healthy donors as well as donors with a disease, and its performance has been evaluated over multiple steps as depicted in the graphical abstract shown in An AE network  between the Input layer and the Modality encoding layer in the encoder network becomesTPAE) between the input layer and the first hidden layer becomesTPReduction) in the encoder network is given byReconstruction layer of the decoder network. Hence, the total reduction  in the number of trainable parameters in UMINT is given bySimilar to an AE network, UMINT also tries to reconstruct the original input as explained in We have further recorded the execution time taken by both UMINT and the different variations of AE for integration of single cell multi-omics data. This experiment has been repeated multiple times with different training and test datasets to ensure stability of results. As shown in Input Layer of UMINT which has then been used to reconstruct the input data. On a similar note, RNA and protein expression data have been stacked together to form the input to the AE-based networks. The AE-based networks reconstruct the combined input by passing it through a series of layers. In the process, they learn to extract useful features at the bottleneck layer, where a latent embedding of 64 dimensions is produced, similar to UMINT. The models UMINT, AE, DAE and SAE have been trained keeping all hyper-parameter values identical. For each modality, the amount of correlation between the original data and its reconstruction, has then been computed for UMINT and all the AE-based models using Pearson correlation coefficient. We have then defined an Overall Reconstruction Score (ORS) to assess the reconstruction performance of UMINT against that of the AE-based models, based on the omics modalities used for integration as followsρi is the Pearson correlation coefficient value between the pairwise distances in the original ith data modality and the pairwise distances in its reconstructed counterpart. As shown in p-values as recorded in Initially, the performance of UMINT has been compared with that of a standard AE and its variations  with respect to their reconstruction capability and the strength of the latent low-dimensional embeddings produced at the bottleneck layer by each network. As mentioned earlier, RNA and protein expression data form the input to different modules of the Thereafter, we have compared the latent low-dimensional embedding produced by UMINT with that produced by a standard AE and its variations. Once trained to create a lossy reconstruction of the input, the latent representation has been extracted from the bottleneck layer of UMINT and all AE-based models. Cell-type clustering on this latent embedding using k-means and agglomerative hierarchical clustering has been performed to validate the effectiveness of UMINT and compare it with AE-based models using ARI and FMI scores, as discussed earlier.cbmc8k and bmcite30k datasets, the latent representation produced by UMINT has been more representative of the cell clusters when compared with that of AE-based methods. For MALT10k dataset, when hierarchical clustering algorithm is used, UMINT embedding has produced similar ARI and FMI scores to that obtained on embedding produced by AE-based methods. This is indicated by both ARI and FMI scores as shown in p-values obtained using UMINT and the AE-based models on these three datasets for both hierarchical and k-means clustering algorithms have been shown in We have observed that for Subsequently, we have compared the performance of UMINT with three other state-of-the-art methods - Seuratv4 , have been used for this purpose. Pre-processed datasets have been collected from our earlier work  from TCGA portal . We have used UMINT to integrate heterogenous single cell omics modalities and extract meaningful projections at reduced dimensions. These features have been further used for clustering. The effectiveness of UMINT has been first demonstrated on four publicly available CITE-seq datasets, and compared on three of them with some other state-of-the-art algorithms used for single cell multi-omics integration. One of the three datasets used for benchmarking corresponds to MALT rare disease, which establishes the applicability of UMINT on rare diseases as well. Thereafter, the performance of UMINT has been assessed on an auxiliary dataset containing paired gene expression and ATAC-seq assays.The strengths or advantages of UMINT are many-fold. UMINT-generated latent embedding has been proved to produce better clustering than that generated using AE-based methods. UMINT has a light-weight architecture in terms of the number of trainable parameters. Even then, UMINT-generated reconstruction has been better than that produced by AE-based methods. When evaluated against other state-of-the-art algorithms, UMINT has displayed superior performance over every other method used for comparison, across most of the evaluation criteria on all the three CITE-seq datasets. The fact that UMINT can integrate both CITE-seq, and paired RNA-seq and ATAC-seq data fortifies its strength over other existing single cell multi-omics integration methods. Moreover, UMINT does not make assumptions about the underlying data distribution, thus making it more robust. Finally, UMINT has been found to be competent enough to integrate bulk multi-omics datasets too. It has been able to produce better reconstructions for bulk omics data than that obtained using a standard AE. UMINT-extracted features from bulk multi-omics data, have also shown superior classification of tumour and normal samples. Integration of both single cell and bulk multi-omics datasets imply that UMINT supports integration of widely heterogenous and varying number of omics modalities. Very few such integration methods exist that can efficiently integrate features from both single cell and bulk multi-omics, and can handle variable number of omics layers. UMINT’s capacity to embed features from healthy and disease omics (including a rare disease) also demonstrates its applicability across varying health conditions.bmcite30k dataset well (though the inherent batch effect in this dataset is subject to further investigations), while for kotliarov50k data, integration has been compromised by a tolerable amount due to batch effect. Further, there is a huge imbalance of features in CITE-seq, and the paired RNA-seq and ATAC-seq data. Despite this imbalance, the overall embedding produced by UMINT remains unaffected. In the current scope of work, we have not explored the integration of other high throughput omics modalities like spatial transcriptomics. Sequencing-based spatial transcriptomics data like 10x Visium are still not available at a single cell resolution. They are at spot level which may contain around 10–30 cells per spot on an average which hinders pairing of input samples (cells and spots). Presently, deconvolution methods are still a better choice for interrogating spatial trancriptomics with single cell transcriptomics. We have also not optimized the model at this stage for image-based data, hence multi-omics spatial data, like NanoString GeoMx, MERSCOPE using MERFISH technology, cannot be still used for integration. Inclusion of such omics layer(s) will need inclusion of Convolutional Neural Network (CNN)-based DL models into the existing UMINT architecture. This would further allow us to better understand the overall contribution of each omics layer at a single cell and spatial level to decipher regulatory systems biology on top of scRNA-seq, scATAC-seq and protein expression data with a spatial location. The potentiality of UMINT to select features from each of the input modalities has also not been explored in the current scope of work. Instead, UMINT has been used to extract relevant features from the integrated data at a low dimension. All these remain as a future extension and a scope for improvement for UMINT to identify key molecular anchors in development and disease biology.UMINT, however, is susceptible to batch effects to some extent. It has been able to correct batches for Nevertheless, UMINT can capture better variability among high-dimensional datasets and produce robust low-dimensional embedding which can significantly assist further downstream analyses. A reduction in the number of trainable parameters also makes UMINT far less computationally expensive than existing neural network models based on AEs. Thus, we are able to provide a robust and efficient unsupervised deep learning model for single cell multi-omics integration.


Liquid biopsy, particularly cell-free RNA (cfRNA), has emerged as a promising non-invasive diagnostic tool for various diseases, including cancer, due to its accessibility and the wealth of information it provides. A key area of interest is the composition and cellular origin of cfRNA in the blood and the alterations in the cfRNA transcriptomic landscape during carcinogenesis. Investigating these changes can offer insights into the manifestations of tissue alterations in the blood, potentially leading to more effective diagnostic strategies. However, the consistency of these findings across different studies and their clinical utility remains to be fully elucidated, highlighting the need for further research in this area.In this study, we analyzed over 350 blood samples from four distinct studies, investigating the cell type contributions to the cfRNA transcriptomic landscape in liver cancer. We found that an increase in hepatocyte proportions in the blood is a consistent feature across most studies and can be effectively utilized for classifying cancer and healthy samples. Moreover, our analysis revealed that in addition to hepatocytes, liver endothelial cell signatures are also prominent in the observed changes. By comparing the classification performance of cellular proportions to established markers, we demonstrated that cellular proportions could distinguish cancer from healthy samples as effectively as existing markers and can even enhance classification when used in combination with these markers.Our comprehensive analysis of liver cell-type composition changes in blood revealed robust effects that help classify cancer from healthy samples. This is especially noteworthy, considering the heterogeneous nature of datasets and the etiological distinctions of samples. Furthermore, the observed differences in results across studies underscore the importance of integrative and comparative approaches in the future research to determine the consistency and robustness of findings. This study contributes to the understanding of cfRNA composition in liver cancer and highlights the potential of cellular deconvolution in liquid biopsy.The online version contains supplementary material available at 10.1186/s40246-023-00537-w. Liquid biopsy, the molecular analysis of body fluids, has emerged as a promising tool in cancer research, offering a more accessible assessment of patient health status compared to traditional tissue biopsies. Advancements in technology have enabled extensive genomic and transcriptomic analysis of DNA and RNA, especially in blood –4. AmongCellular deconvolution, a powerful computational approach, is used to determine the cellular origin of RNA in mixed transcriptomic data, such as bulk RNA-seq . A multiCellular deconvolution using cfRNAs has been demonstrated to yield promising results in an increasing number of studies , 12–15. Liver cancer, predominantly represented by hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), constitutes the second most lethal cancer type . In 2020Current cellular deconvolution methods typically rely on reference data that are advised to encompass all cell types present in the RNA mixture . HoweverHere, by employing targeted cellular deconvolution, we analyzed over 350 blood cell-free RNA-seq samples gathered from four different studies –26, inclWe used four publicly available cfRNA datasets containing blood RNA-seq samples from liver cancer patients and healthy donors, comprising 221 samples –26 (TablMetadata files were downloaded from Sequence Read Archive (SRA) database and supplemented by us with additional information from the corresponding studies. Datasets generated by Zhu et al. and Chen et al. provided gene count matrices, the generations of which are detailed in the corresponding studies and were downloaded from the Gene Expression Omnibus (GEO) database under the accession numbers GSE142987 and GSE174302, respectively (Table Ensembl gene IDs were converted to HGNC (Human Genome Organization Gene Nomenclature Committee) symbols using the R (version 4.1.2)  package In order to generate principal component analysis (PCA) plots, samples were separately normalized with variance stabilizing transformation using the function “vst” from the R package DESeq2 (1.34.0) . Plots wCell-type deconvolution was performed using the R package Bisque (version 1.0.5)  with a rFinally, decomposition was carried out using the function “ReferenceBasedDecomposition” from the package Bisque with the parameter “use.overlap = FALSE” for each dataset. The Chen et al. dataset with the additional non-liver solid tumor samples was analyzed separately and was not used in the modeling steps.r) and corresponding confidence intervals were generated with the function “wilcox_effsize” using the parameters “alternative = ”greater”,” “paired = FALSE,” “nboot = 100″ and “ci = TRUE” from the R package rstatix.To test if hepatocyte proportions were greater in liver cancer samples compared to other samples, a one-sided, unpaired Wilcoxon test (Wilcoxon rank-sum test) was calculated using the deconvolution results of all samples with the function “wilcox_test” from the R package rstatix (version 0.7.2) . To thisTo test if the hepatocyte proportions were greater in the plasma compared with extracellular vesicles (EVs) of five liver cancer patients, a one-sided, paired Wilcoxon test (Wilcoxon signed-rank test) was performed as previously described, with the only change being the parameter “paired = TRUE.” Effect size and corresponding confidence intervals were calculated as previously described, with the only change being “paired = TRUE.” The results were visualized with the R packages rstatix, ggpubr (version 0.5.0)  and ggplTo analyze the feasibility of classifying liver cancer and healthy samples based on hepatocyte proportions, we tested 20 hepatocyte proportion cutoffs ranging from 0.2 to 0.4 in all cfRNA datasets—with samples above the cutoff classified as liver cancer (LC) patients and healthy donors (HD) if otherwise. Accuracy, sensitivity and specificity were computed at each cutoff with the function “confusionMatrix” from the R package caret (version 6.0–93)  and wereWe built random forest models to both determine the relative importance of cell types between biological conditions, sources of samples and to evaluate the diagnostic capabilities of various predictors. First, random forest models were built with each dataset using the cell-type proportions as input using the function “randomForest” with the parameter “importance = TRUE” from the R package randomForest (version 4.7–1.1) . AfterwaTo assess the performance of predictors, we trained a model with the Roskams-Hieter et al. dataset, chosen for its balanced structure and informative sample composition  votes generated by the random forest algorithm using the function “roc” with the parameters “ci = TRUE” from the R package pROC (version 1.18.0) . The ROCSubsequently, the model was used to predict the probabilities of the class to which the samples belong from other cfRNA datasets with the “predict” function using the parameter “type = ”prob”” from the R package randomForest. The per dataset predicted probabilities were used as input to generate ROC curves as previously described. For the calculation of a total AUROC, all the predicted sample probabilities were used as input. Sample classification was performed using the cutoff value from the training dataset.Equivalent to the random forest modeling, a logistic regression model with elastic net regularization was trained on the Roskams-Hieter et al. dataset with the cell-type proportion and later tested on three other cfRNA datasets. First, the optimal alpha and lambda hyperparameter values were determined, where alpha controls the extent of L1 and L2 regularizations and lambda determines the magnitude of the regularization. This was achieved by performing repeated cross-validation  as previously described using the function “train” from the R package caret, where the parameters “method = ”glmnet”,” “metric = ”ROC,” “tunelenght = 10″ and “standardize = TRUE” were used. The resulting sample class probabilities were averaged across all the repeated cross-validations and used to assess the performance of the model. This was achieved by constructing a ROC curve as previously described  . To deteFinally, the generated model was tested on remaining cfRNA datasets using the function “predict” with the parameter “type = ”response”” from the R package glmnet. The predicted probabilities were used to generate ROC curves and calculate the total AUROC measurement as previously described. Sample classification was done using the cutoff value from the training dataset.To assess the performance of the generated models, firstly, the AUROC was measured for each prediction with the function “calc_auc” from the R package plotROC (version 2.3.0) . AUROC cTo ascertain the influence of age on the accuracy of sample classification with the logistic regression model, we restricted our evaluation to male and female samples from healthy donors and liver cancer patients in the datasets generated by Roskams-Hieter et al. and Zhu et al., attributed to their comprehensive sample annotations. Visualization of sample metadata was conducted using the packages ggplot2 and ggpubr. Age-related differences in misclassified samples were statistically analyzed using an unpaired, two-sided Wilcoxon test as already described with the argument “alternative = ”t”” used in the “wilcox_test” and “wilcox_effsize” functions from the “rstatix” package.We further explored the potential influence of the blood collection date on classification accuracy of the logistic regression model within the dataset from Block et al. as the dataset provided blood collection dates for liver cancer patient samples. First, liver cancer samples collected post-12–12-2010 were excluded, leading to the removal of six samples. Then, liver cancer samples obtained post-12–12-2016 were also omitted, resulting in four additional samples being excluded. Finally, in addition to testing the logistic regression model on the entirety of the Block et al. dataset, we also tested the model on the aforementioned filtered subsets as already described. Results were visualized using the ggplot2 and ggpubr packages as already described.In order to investigate the liver cell type-specific contribution to the cell-free transcriptome in blood and its perturbations during carcinogenesis, we performed targeted cellular deconvolution on four cfRNA datasets containing 112 samples from healthy donors, 109 samples from liver cancer patients and 157 samples from colorectal cancer (CRC), stomach cancer (STAD), lung cancer (LUAD) and esophageal cancer (ESCA) patients (Table r < 0.5), while in the Chen et al. and Zhu et al. datasets, the differences had a large effect size (r >  = 0.5)  measure was used to rank the features Fig. . In the Next, we investigated the difference in cell type proportions between five plasma and extracellular vesicle (EV) samples from the same liver cancer patients in the Block et al. dataset  age disparities: between female liver cancer samples and their male counterparts, and between female healthy donors and female liver cancer samples , Chen et al. (5 markers) and a combination of both (15 markers). Since the logistic regression modeling yielded comparatively less accurate results, we decided to continue with a random forest model. A random forest model was trained using the Roskams-Hieter et al. dataset using either the marker sets separately or combined  that previously displayed high importance in differentiating biological conditions to the combined gene marker random forest model. As before, the integrated model was trained with the Roskams-Hieter et al. dataset .The targeted cellular deconvolution provided a snapshot of the contribution of liver cell types to the blood cell-free transcriptome. By extrapolating the cell-type proportions, liver-targeted cellular deconvolution also provided the changes in liver cell-type proportions during cancerogenesis. As for the liver , the celWe further investigated the importance of other liver cell types in classification. Hence, we fitted random forest models with each dataset and, through the MDA measurement, ordered the cell types by importance in each dataset. While the high importance displayed by hepatocytes in two datasets was expected, it was interesting to note that in two other datasets, other cell types, notably LSECs and PECs, were more important in classification. PECs were also highly important in all four datasets, further strengthening the argument for an increased focus on the liver endothelial cells. While LSECs have recently received attention for their role in liver cancer cancerogenesis  and poteA recent consideration in the field of liquid biopsy has been the source of the cell-free RNAs in blood, with the main choices being between plasma and EVs , 23, 53.We also endeavored to improve upon the results displayed by the hepatocyte proportion cutoff. Since we noticed that hepatocytes did not have a strong discriminatory ability in the Block et al. dataset and other cell types also displayed high importance during classification, we sought to incorporate the proportions of other liver cell types as well into developing more robust diagnostic models. Logistic regression with an elastic net regularization model built with all the cell-type proportions of Roskams-Hieter et al. datasets as input showed robust results across all three tested datasets. Compared with the hepatocyte proportion cutoff-based classification, the overall accuracy was improved, proving further the importance of other, non-hepatocyte cell types as well. To assess the performance of targeted cellular deconvolution models, we concurrently analyzed some of the reported cell-free gene marker sets for liver cancer diagnosis. The gene marker sets were modeled both individually and in combination. Random forest models built using gene marker expression in the Roskams-Hieter et al. dataset displayed comparable accuracy to the deconvolution-based model, with the latter slightly outperforming the former. While the cellular deconvolution model displayed higher sensitivity, the gene marker models  outperformed it in terms of specificity. These results not only show the validity of both methods but also underline the remarkable similarity and stability of blood cfRNA samples generated in different environments and drawn from patients with different etiologies   plot. Plots were generated using variance stabilized counts prior to batch correction for Roskams-Hieter et al. (A), Chen et al. (B), Zhu et al. (C) and Block et al. (D) datasets. HD, healthy donor; LC, liver cancer. Figure S2: Performance of model training with Roskams-Hieter et al. (2022) dataset. The results are represented with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values. Figure S3: Differences in cellular sources of cfRNA between matched plasma and extracellular vesicle (EV) samples in the Block et al. (2022) dataset. (A) Importance of cell types in the classification of plasma and EV samples as measured by the Mean Decrease in Accuracy (MDA) value from the random forest model. Cell types were ordered in descending order of MDA. (B) Hepatocyte proportion differences between matched plasma and EV samples from each patient. Figure S4: Performance of model testing on each dataset represented with receiver operator characteristic (ROC) curves and area under ROC (AUC) values. The targeted cellular deconvolution (A), Roskams-Hieter et al. gene markers (B), Chen et al. gene markers (C) and combined gene markers (D) models were trained with the Roskams-Hieter et al. (2022) dataset and tested on the remaining datasets. The results were assessed with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values . The error bars depicted in the figure represent the 95% confidence intervals. Figure S5: Confusion matrices of model testing. Confusion matrices for the targeted cellular deconvolution (A), Roskams-Hieter et al. gene markers (B), Chen et al. gene markers (C) and combined gene markers (D) model testing. The left-hand top and right-hand bottom numbers represent the positive and negative predictive values, respectively. The numbers on the top and bottom represent the success rate of classifications. Figure S6: Age distribution in Roskams-Hieter et al. and Zhu et al. datasets. Age distribution of female and male healthy donor (HD) and liver cancer (LC) samples in Roskams-Hieter et al. and Zhu et al. datasets (only these datasets contained comprehensive per sample annotations) (A), all samples (B), samples misclassified by the targeted cellular deconvolution model. Figure S7: Influence of sample collection date on model performance. Performance of targeted cellular deconvolution model on Block et al. dataset (only this dataset contained bleed date information): full, after removing liver cancer samples generated after 2010 and after further removing liver cancer samples generated after 2016. The results are represented with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values. Additional file 2. Detailed overview of liver cancer patient characteristics across datasets.Additional file 3. Results of statistical analyses.Additional file 4. Classification and modeling results.


Castration-resistant prostate cancer (CRPC) represents one type of advanced prostate cancer (PCa) with a median survival time of 1–2 years. Currently, there is a lack of reliable gene panels in predicting hormone treatment (HT) responses due to limited knowledge of CRPC-specific tumor-microenvironment (TME) characteristics.In this study, we first screened for up-regulated genes in CRPC samples using bulk-sequencing data retrieved from TCGA online database, and further investigated the expression status of these genes in four sets of downloaded single-cell RNA sequencing (scRNAseq) data: GSE117403 containing 16 normal human prostate samples; GSE141445 containing 13 PCa samples; GSE176031 containing 11 PCa samples and GSE137829 containing 6 CRPC samples.PEG10+ neuroendocrine cells, elevated expression of PPIB/CCDC74A/GAPDH/AR genes in tumor cells, increased expression of FAP/TGFB1 in cancer-associated fibroblasts (CAFs), suppressed immune environment featured by enhanced M2 macrophage polarization, T cell exhaustion and increased number of regulatory B cells. We further established a 12-gene panel using these characteristics and showed that this panel could separate CRPC samples from PCa samples (AUC of 0.78), and CRPC patients with higher panel scores tended to have treatment failure or progression .We identified a series of CRPC-specific TME characteristics including an enriched number of Based on these unique TME characteristics of CRPC, we established a prediction tool for estimating the duration of HT responses in PCa treatment. Our results suggest mechanisms by which prostate cancer becomes castrate resistant. Further study of PEG10 (and/or others) to evaluate therapeutic efficacy should be considered. Increased number of PEG10+ neuroendocrine cells is associated with recurrence of HT PCa patientsA 12-gene panel could be used to predict duration of HT responses in PCa treatment In 2022, there are 268,490 new PCa patients in the United States 1st which s in men . TreatmeUnderstanding the CRPC-specific tumor-microenvironment (TME) features is the key to developing an accurate prediction tool. Recently, with the help of single-cell RNA sequencing (scRNAseq) technology, one can elucidate the specific TME characteristics and intercellular communications in CRPC samples at a cellular level. Dong et al.  revealedIn this study, we re-investigated TME features using 4 sets of downloaded scRNAseq data involving normal prostate samples, PCa samples, and CRPC samples, combined with bulk-sequencing data of CRPC samples from TCGA database and discovered a series of CRPC-specific features. Moreover, we established a 12-gene panel based on these features and showed that this panel had a good performance in predicting HT responses during PCa treatments.Two patients diagnosed with primary PCa and one patient diagnosed with CRPC were involved in this study. Their frozen tumor tissue sections were obtained from Pathology department of Shenzhen People’s Hospital. This study was performed in accordance with the guidelines of the 1975 Declaration of Helsinki.https://xenabrowser.net/). Normal samples were determined as ‘Solid Tissue Normal’ in ‘sample_type.sample’ term; tumor samples were determined as the rest in ‘sample_type.sample’; samples with recurrence were determined as tumor samples with ‘YES’ in ‘new_tumor_event_after_initial_treatment’ term; samples without recurrence were determined as tumor samples with ‘NO’ in ‘new_tumor_event_after_initial_treatment’ term; HT_YES samples were determined as recurrent samples with ‘Hormone Therapy’ in ‘therapy_type’ , and HT_NO samples were determined as non-recurrent samples with ‘Hormone Therapy’ in ‘therapy_type’.Gene expression data (counts and FPKM: fragments per kilobase of exon per million mapped fragments) and sample phenotype data of the prostate adenocarcinoma samples (TCGA-PRAD) was retrieved from UCSC-Xena database  to raw-count reads of HT_YES and HT_NO samples. Log2FC of 1 and p.adjust value of 0.05 were used as significance cutoff.www.ncbi.nlm.nih.gov/geo/) under their accession numbers , and mean expression values for all genes were calculated for each subgroup. Pearson correlation value between two subgroups was calculated using R cor.test function and the correlation results between all subgroup combinations were visualized using R ‘pheatmap’ package.Lists of TCGA and CPGEA module genes were retrieved from Wang et al. . For eacSubgroup-featured genes were determined as genes with the highest expression values across all subgroups. Gene expression values were first normalized using log2(CPM + 1), and for a given gene A, if its expression values were significantly higher in cells from subgroup B (dataset C) compared to the values from other subgroups (significance was determined using R Limma-trend package), and gene A was expressed in over 20% of the cells from subgroup B, gene A was considered as one of the B subgroup-featured genes. KEGG enrichment analysis of subgroup-featured genes was performed using ‘clusterProfiler’ R package (p.adjust< 0.05), and top enriched KEGG terms were selected and displayed.The CPRC patient involved in the validation process of this study was enrolled in Shenzhen People’s Hospital. This CRPC patient had a laparoscopic radical prostatectomy in the year 2018, followed by three rounds of endocrine therapy , and the progression happened in the year 2022. This is a luminal CRPC based on the diagnosis.2O2 at room temperature for 10 min. The following two primary antibodies were used in this study: anti-EPCAM  and anti-PEG10 . After being rinsed with 1X PBS at room temperature for 3 times, the sections were first incubated with primary antibodies for 2h at room temperature, followed by 3 times of 1X PBS rinsing, 1h of incubation with goat anti-rabbit H&L Cy5 secondary antibody (Abcam ab6564), and 3 more times of 1X PBS rinsing. The following stains  were used in the final staining step.Tumor tissue sections from two PCa patients and one CRPC patient were obtained from the pathology department of Shenzhen People’s Hospital. Prior to antibody incubation, all sections were washed in 3% Hwww.proteinatlas.org) website, in which secreted proteins were determined as ‘Secreted’ in ‘Predicted location’ term, and membrane proteins were determined as ‘Plasma membrane’ in ‘Predicted location’ term; 2) Protein-Protein interaction information was retrieved from BioGRID (www.thebiogrid.org) database [LR intensities between different subgroups in GSE137829 dataset were calculated as follows: 1) Location information of all proteins was retrieved from THPA  values were calculated using auc function.Supplementary Table 1. A further transcriptome-wide screening of differentially expressed genes was performed using DESeq2 [Supplementary Table 4), and their relative expression values across these HT-YES/HT-NO samples are listed in To screen for differentially expressed genes in CRPC patients, we first retrieved gene expression data of prostate adenocarcinoma samples from The Cancer Genome Atlas (TCGA-PRAD). Two sets of samples were selected: PRAD samples with recurrence after hormone treatment  and PRAD samples without recurrence after hormone treatment . Detailed sample information is listed in g DESeq2 , and theTo investigate CPRC-specific TME features from single-cell levels, we retrieved four scRNAseq datasets from GEO database: GSE117403 containing expression profiles of 105,208 cells from 16 normal human prostate samples ; GSE1760Due to the disturbance of batch effects across these four datasets , we performed scRNAseq analysis separately to these four datasets instead of the integrating them together. To compare gene expression levels across these four datasets, a log2(CPM + 1) normalization was first performed to all involved cells, followed by a transcriptome-level comparison using Limma-trend method ,17. All EPCAM/KRT19/KRT18; Endothelial cells  are marked using PECAM1/VWF/ACKR1; Stroma cells  are marked using TAGLN/ACTA2/COL1A2; Myeloid cells  are marked using CD14/FCGR3A/CD163; Mast cells  are marked using TPSAB1/TPSB2/MS4A2; T cells  are marked using CD3D/CD3E/GZMA; B cells  are marked using CD79A/MS4A1/DERL3, as summarized in The following standard seRNAseq analysis procedures were applied: a matrix reconstruction step using seurat; duplet removal step using DoubletFinder ; an inteKRT14/DST/KRT15/KRT5/RGCC), luminal cells (marked by MSMB/KLK3/ACPP/PLA2G2A/KLK2), neuroendocrine cells (marked by CHGA/GRP/CALCA/SCG2/TPH1), cycling cells (marked by MKI67/TOP2A/TUBA1B/HMGB2/CENPF), other1 cells  and other2 cells   . Among eSupplementary Figure 1. Compared to epithelial cells in normal prostate samples (GSE117403), more copy number variance features were found in two tumor samples  and CRPC samples (GSE137829).We performed inferCNV analysis to all epithelial cells from these four datasets using expression profiles of t cells , and the results are listed in Similarity among subgroups from each dataset was evaluated using Pearson correlation analysis . The results are summarized in Supplementary Table 2). Luminal subgroups have relatively higher TCGA/CPGEA module scores compared to those in basal/neuroendocrine/others subgroups; tumor/CRPC subgroups have relatively higher module scores compared to those in normal subgroups. These results further support the robustness of the analysis process.To investigate the tumor-associated features of these subgroups, we applied two PCA-related gene panels  to theseSubgroup-featured genes  are further summarized  and CCDC74A (Coiled-coil domain containing 74 A) have higher expression values in neuroendocrine/luminal subgroups from CRPC samples compared to those from other subgroups  and CRPC tissue (Patient 3) with anti-EPCAM (green) and anti-PEG10 (red) antibodies, and the immunostaining results are shown in PEG10-expressing epithelial cells (green + red) are examined in CRPC tissue compared to these in PCa tissues, which further supports our scRNAseq results.The expression status of the top 10 upregulated genes in HT-YES samples is also examined among epithelial subgroups across all 4 datasets. Neuroendocrine subgroup from CRPC samples has the highest expression scores of these genes, followed by luminal subgroup from CRPC samples, as illustrated in ubgroups . To furtPPIB  - GAPDH  ranks 1st among all the L-R interactions, indicating that this interaction might play an important role in CRPC progression.Secreted ligand is one of the most direct methods that target cells use to reshape their microenvironments. To investigate the communication status among CRPC epithelial subgroups, we calculated Ligand-Receptor (L-R) intensity between different subgroups, and the results are summarized in PEG10/CCDC74A/PPIB/GAPDH have higher expression values in neuroendocrine and luminal subgroups from CRPC samples . Moreover, we also find that AR (Androgen receptor), a transcription factor that could be regulated by GAPDH [PPIB-GAPDH-AR combination might be a key regulator in CRPC progression. Together with EPCAM , we established a 6-gene panel containing PEG10/CCDC74A/PPIB/GAPDH/AR/EPCAM, and further examined the performance of this panel in the TCGA cohort, as shown in As demonstrated previously,  samples . Moreoveby GAPDH , has thep = 0.064), as shown in p = 0.038), as shown in Specifically, we further explored the relationship between expression scores of this 6-gene panel and recurrence time. Among tumor samples with recurrence, samples with shorter (time < = median) recurrence time have higher panel scores compared to samples with longer (time > median) recurrence time ; samples with higher panel scores (score > = median) have shorter recurrence time compared to these with lower (score < median) panel scores . Pearson correlation analysis between 6-gene panel scores and recurrence time reveals a weak (not significant) negative correlation , CAF cells (marked by FAP/ACTA2/PDGFRB), and SMC   and PCa samples (GSE141445); among CAF subgroups, both FAP and TGFB1 expressions are higher in cells from CRPC samples (GSE137829) compared to cells from PCa samples (GSE141445 and GSE176031), suggesting an enhanced CAF conversion process in CRPC samples.Stroma cells were clustered into fibroblasts (marked by 5/MYH11) . Among sCD14/FCGR3A/HLA-DRA), macrophages (marked by CD163/MRC1/CD68/CD86), and dendritic cells   . Among m samples , suggestIL2RA/FOXP3/IKZF2/CD4), effector T cells (marked by GZMK/GZMA/GZMB), memory T cells (marked by CCR7/TCF7/LEF1), and natural killer (NK) cells (marked by -/NKG7/GNLYCD3D)  . Among T samples . The expCD79A/CD79B/MS4A1) and plasma B cells (marked by CD79A/DERL3)  . Among BFAP/IL10/TGFB1/TIGIT/LAG3/PDCD1) to the original 6-gene panel and further explored the performance of this panel in TCGA cohort. Among TCGA PRAD samples, this 12-gene panel has the highest expression scores in HT tumor samples with recurrence, which is consistent with the previous 6-gene panel; specifically, the expression score is significantly higher in tumor samples compared to these in normal samples ; significantly higher in PCa samples with recurrence compared to these in PCa samples without recurrence ; significantly higher in HT samples with recurrence compared to these in HT samples without recurrence , as shown in We added 6 more genes identified from CRPC-specific TME features .In this study, through a combined analysis of bulk-sequencing data and 4 sets of scRNAseq data, we successfully identified a series of CRPC-specific features such as increased number of PEG10 in neuroendocrine prostate cancer  have been reported in many studies. Kim et al. [PEG10 is associated with neuroendocrine differentiation after AR axis-directed therapy, and Akamatsu et al. [PEG10 could promote the progression of NEPC through stimulating cell-cycle progression via RB1 and TP53 loss and regulating Snail expression via TGF-beta signaling. In this study, we found that PEG10 is up-regulated in both HT PCa samples with recurrence and neuroendocrine cells from CRPC samples, suggesting that PEG10 is a promising target in CRPC exploration.The involvement of placental gene m et al.  showed tu et al.  reportedAR in CRPC progression has been observed for a long time. Visakorpi et al. [PPIB-GAPDH pair ranked 1st among all communication combinations between cells from neuroendocrine subgroup and luminal subgroup in CRPC samples. GAPDH could enhance the transcriptional activity of AR in prostate cancer cells [PPIB could regulate cell-cycle related genes including CCDC74A [PPIB/GAPDH/AR/CCDC74A gene set might play an important role in regulating CRPC progression.AR-signaling axis is crucial in all stages of prostate cancer, and the involvement of i et al.  observedi et al.  reportedi et al. , howeveri et al. , 27, 28,i et al.  have fouer cells , and PPI CCDC74A  which fu CCDC74A . Taken tDuring tumor progression, various components from TMEs co-contribute to this process. CAF cells represent one of the most abundant components and play important roles in tumorigenesis, progression, metastasis and recurrence in prostate cancer . CAF celRegarding immune features, we discovered a series of immunosuppressed phenotypes in CRPC samples such as enhanced M2 macrophage polarization process, enhanced Tcell exhaustion process, and increased number of regulatory B cells, suggesting that repressed immune system might facilitate CRPC progression. It is noteworthy that none of the immune-related cells are identified in GSE117403, which is consistent with its original publication . This miCRPC might arise from a small subset of cancerous cells , and dueClick here for additional data file.