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Tea plants are a perennial crop with significant economic value. Chlorophyll, a key factor in tea leaf color and photosynthetic efficiency, is affected by the photoperiod and usually exhibits diurnal and seasonal variations. In this study, high-throughput transcriptomic analysis was used to study the chlorophyll metabolism, under different photoperiods, of tea plants. We conducted a time-series sampling under a skeleton photoperiod (6L6D) and continuous light conditions (24 L), measuring the chlorophyll and carotenoid content at a photoperiod interval of 3 h (24 h). Transcriptome sequencing was performed at six time points across two light cycles, followed by bioinformatics analysis to identify and annotate the differentially expressed genes (DEGs) involved in chlorophyll metabolism. The results revealed distinct expression patterns of key genes in the chlorophyll biosynthetic pathway. The expression levels of CHLE (magnesium-protoporphyrin IX monomethyl ester cyclase gene), CHLP (geranylgeranyl reductase gene), CLH (chlorophyllase gene), and POR (cytochrome P450 oxidoreductase gene), encoding enzymes in chlorophyll synthesis, were increased under continuous light conditions (24 L). At 6L6D, the expression levels of CHLP1.1, POR1.1, and POR1.2 showed an oscillating trend. The expression levels of CHLP1.2 and CLH1.1 showed the same trend, they both decreased under light treatment and increased under dark treatment. Our findings provide potential insights into the molecular basis of how photoperiods regulate chlorophyll metabolism in tea plants.

To address the challenge of limited throughput with traditional toxicity testing, a newly developed high-throughput transcriptomics (HTT) platform, together with a 5-day in vivo rat model, offers an alternative approach to estimate chemical exposures and provide reasonable estimates of toxicological endpoints. This study contains an HTT analysis of 18 environmental chemicals with known liver toxicity. They were evaluated using male Sprague Dawley rats exposed to various concentrations daily for five consecutive days via oral gavage, with data collected on the sixth day. Here, we further explored the 5-day rat model to identify potential gene signatures that can differentiate between toxic and non-toxic liver responses and provide us with a potential histopathological endpoint of chemical exposure. We identified a distinct gene expression pattern that differentiated non-hepatotoxic compounds from hepatotoxic compounds in a dose-dependent manner, and an analysis of the significantly altered common genes indicated that toxic chemicals predominantly upregulated most of the genes and several pathways in amino acid and lipid metabolism. Finally, our liver injury module analysis revealed that several liver-toxic compounds showed similarities in the key injury phenotypes of cellular inflammation and proliferation, indicating potential molecular initiating processes that may lead to a specific end-stage liver disease.

Background Prolonged exposure to toxic heavy metals leads to deleterious health outcomes including kidney injury. Metal exposure occurs through both environmental pathways including contamination of drinking water sources and from occupational hazards, including the military-unique risks from battlefield injuries resulting in retained metal fragments from bullets and blast debris. One of the key challenges to mitigate health effects in these scenarios is to detect early insult to target organs, such as the kidney, before irreversible damage occurs. Methods High-throughput transcriptomics (HTT) has been recently demonstrated to have high sensitivity and specificity as a rapid and cost-effective assay for detecting tissue toxicity. To better understand the molecular signature of early kidney damage, we performed RNA sequencing (RNA-seq) on renal tissue using a rat model of soft tissue-embedded metal exposure. We then performed small RNA-seq analysis on serum samples from the same animals to identify potential miRNA biomarkers of kidney damage. Results We found that metals, especially lead and depleted uranium, induce oxidative damage that mainly cause dysregulated mitochondrial gene expression. Utilizing publicly available single-cell RNA-seq datasets, we demonstrate that deep learning-based cell type decomposition effectively identified cells within the kidney that were affected by metal exposure. By combining random forest feature selection and statistical methods, we further identify miRNA-423 as a promising early systemic marker of kidney injury. Conclusion Our data suggest that combining HTT and deep learning is a promising approach for identifying cell injury in kidney tissue. We propose miRNA-423 as a potential serum biomarker for early detection of kidney injury. Graphical Abstract

Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K -nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.

Background The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line. Results We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p -values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events. Conclusions This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.

High-throughput transcriptomics has advanced through the introduction of TempO-seq, a targeted alternative to traditional RNA-seq. TempO-seq platforms use 50 nucleotide probes, each specifically designed to target a known transcript, thus allowing for reduced sequencing depth per sample compared with RNA-seq without compromising the accuracy of results. Thus far, studies using the TempO-seq method have relied on existing tools for processing the resulting short read data. However, these tools were originally designed for other data types. While they have been used for processing of early TempO-seq data, they have not been systematically assessed for accuracy or compared to determine an optimal framework for processing and analyzing TempO-seq data. In this work, we re-analyze several publicly available TempO-seq data sets covering a range of experimental designs and use corresponding RNA-seq data sets as a gold standard to rigorously assess accuracy at multiple levels. We compare 6 aligners and 5 normalization methods across various accuracy and performance metrics. Our results demonstrate the overall robust accuracy of the TempO-seq platform, independent of data processing methods. Complex aligners and advanced normalization methods do not appear to have any general advantage over simpler methods when it comes to analyzing TempO-seq data. The reduced complexity of the sequencing space, and the fact that TempO-seq probes are all equal length, appears to reduce the need for elaborate bioinformatic or statistical methods used to address these factors in RNA-seq data.

Background The QuantiGene® Plex 2.0 platform (ThermoFisher Scientific) combines bDNA with the Luminex/xMAP magnetic bead capturing technology to assess differential gene expression in a compound exposure setting. This technology allows multiplexing in a single well of a 96 or 384 multi-well plate and can thus be used in high throughput drug discovery mode. Data interpretation follows a three-step normalization/transformation flow in which raw median fluorescent gene signals are transformed to fold change values with the use of proper housekeeping genes and negative controls. Clear instructions on how to assess the data quality and tools to perform this analysis in high throughput mode are, however, currently lacking. Results In this paper we introduce QGprofiler, an open source R based shiny application. QGprofiler allows for proper QuantiGene® Plex 2.0 assay optimization, choice of housekeeping genes and data pre-processing up to fold change, including appropriate QC metrics. In addition, QGprofiler allows for an Akaike information criterion based dose response fold change model selection and has a built-in tool to detect the cytotoxic potential of compounds evaluated in a high throughput screening campaign. Conclusion QGprofiler is a user friendly, open source available R based shiny application, which is developed to support drug discovery campaigns. In this context, entire compound libraries/series can be tested in dose response against a gene signature of choice in search for new disease relevant chemical entities. QGprofiler is available at: https://qgprofiler.openanalytics.eu/app/QGprofiler

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.

Fungal invasive infections are an increasing health problem. The intrinsic complexity of pathogenic fungi and the unmet clinical need for new and more effective treatments requires a detailed knowledge of the infection process. During infection, fungal pathogens are able to trigger a specific transcriptional program in their host cells. The detailed knowledge of this transcriptional program will allow for a better understanding of the infection process and consequently will help in the future design of more efficient therapeutic strategies. Simultaneous transcriptomic studies of pathogen and host by high-throughput sequencing (dual RNA-seq) is an unbiased protocol to understand the intricate regulatory networks underlying the infectious process. This protocol is starting to be applied to the study of the interactions between fungal pathogens and their hosts. To date, our knowledge of the molecular basis of infection for fungal pathogens is still very limited, and the putative role of regulatory players such as non-coding RNAs or epigenetic factors remains elusive. The wider application of high-throughput transcriptomics in the near future will help to understand the fungal mechanisms for colonization and survival, as well as to characterize the molecular responses of the host cell against a fungal infection.

Recent advances in multiplexed single‐cell transcriptomics experiments facilitate the high‐throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep‐learning approaches for single‐cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single‐cell level for unseen dosages, cell types, time points, and species. Using newly generated single‐cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single‐cell Perturb‐seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single‐cell level and thus accelerate therapeutic applications using single‐cell technologies. Synopsis The compositional perturbation autoencoder (CPA) is a deep learning model for predicting the transcriptomic responses of single cells to single or combinatorial treatments from drugs and genetic manipulations. CPA can be trained on highly multiplexed, single‐cell experiments with thousands of conditions to predict unmeasured phenotypes (e.g., specific dose responses). It can generalize to predict responses to small molecules never seen in the training by adding priors on chemical space. Validations using a newly generated combinatorial drug perturbation dataset demonstrate the accuracy of CPA in predicting unseen drug combinations. CPA is also applicable to genetic combinatorial screens, as shown by imputing in silico 5,329 missing combinations in a single‐cell perturb‐seq experiment with diverse genetic interactions. Graphical Abstract The compositional perturbation autoencoder (CPA) is a deep learning model for predicting the transcriptomic responses of single cells to single or combinatorial treatments from drugs and genetic manipulations.