Live-seq enables temporal transcriptomic recording of single cells.

Single-cell transcriptomics (scRNA-seq) has greatly advanced our ability to characterize cellular heterogeneity1. However, scRNA-seq requires lysing cells, which impedes further molecular or functional analyses on the same cells. Here, we established Live-seq, a single-cell transcriptome profiling approach that preserves cell viability during RNA extraction using fluidic force microscopy2,3, thus allowing to couple a cell's ground-state transcriptome to its downstream molecular or phenotypic behaviour. To benchmark Live-seq, we used cell growth, functional responses and whole-cell transcriptome read-outs to demonstrate that Live-seq can accurately stratify diverse cell types and states without inducing major cellular perturbations. As a proof of concept, we show that Live-seq can be used to directly map a cell's trajectory by sequentially profiling the transcriptomes of individual macrophages before and after lipopolysaccharide (LPS) stimulation, and of adipose stromal cells pre- and post-differentiation. In addition, we demonstrate that Live-seq can function as a transcriptomic recorder by preregistering the transcriptomes of individual macrophages that were subsequently monitored by time-lapse imaging after LPS exposure. This enabled the unsupervised, genome-wide ranking of genes on the basis of their ability to affect macrophage LPS response heterogeneity, revealing basal Nfkbia expression level and cell cycle state as important phenotypic determinants, which we experimentally validated. Thus, Live-seq can address a broad range of biological questions by transforming scRNA-seq from an end-point to a temporal analysis approach.

Reference Maps of human ES and iPS cell variation enable high-throughput characterization of pluripotent cell lines.

The developmental potential of human pluripotent stem cells suggests that they can produce disease-relevant cell types for biomedical research. However, substantial variation has been reported among pluripotent cell lines, which could affect their utility and clinical safety. Such cell-line-specific differences must be better understood before one can confidently use embryonic stem (ES) or induced pluripotent stem (iPS) cells in translational research. Toward this goal we have established genome-wide reference maps of DNA methylation and gene expression for 20 previously derived human ES lines and 12 human iPS cell lines, and we have measured the in vitro differentiation propensity of these cell lines. This resource enabled us to assess the epigenetic and transcriptional similarity of ES and iPS cells and to predict the differentiation efficiency of individual cell lines. The combination of assays yields a scorecard for quick and comprehensive characterization of pluripotent cell lines.

A web-based integrative transcriptome analysis, RNAseqChef, uncovers the cell/tissue type-dependent action of sulforaphane.

RNA sequencing (RNA-seq) is a powerful technique for understanding cellular state and dynamics. However, comprehensive transcriptomic characterization of multiple RNA-seq datasets is laborious without bioinformatics training and skills. To remove the barriers to sequence data analysis in the research community, we have developed "RNAseqChef" (RNA-seq data controller highlighting expression features), a web-based platform of systematic transcriptome analysis that can automatically detect, integrate, and visualize differentially expressed genes and their biological functions. To validate its versatile performance, we examined the pharmacological action of sulforaphane (SFN), a natural isothiocyanate, on various types of cells and mouse tissues using multiple datasets in vitro and in vivo. Notably, SFN treatment upregulated the ATF6-mediated unfolded protein response in the liver and the NRF2-mediated antioxidant response in the skeletal muscle of diet-induced obese mice. In contrast, the commonly downregulated pathways included collagen synthesis and circadian rhythms in the tissues tested. On the server of RNAseqChef, we simply evaluated and visualized all analyzing data and discovered the NRF2-independent action of SFN. Collectively, RNAseqChef provides an easy-to-use open resource that identifies context-dependent transcriptomic features and standardizes data assessment.

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.

BACKGROUND: Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data. MAIN BODY: The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. CONCLUSIONS: According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

RGeasy: a reference gene analysis tool for gene expression studies via RT-qPCR.

Gene expression through RT-qPCR can be performed by the relative quantification method, which requires the expression normalization through reference genes. Therefore, it is essential to validate, experimentally, the candidate reference genes. Thus, although there are several studies that are performed to identify the most stable reference genes, most them validate genes for very specific conditions, not exploring the whole potential of the research since not all possible combinations of treatments and/or conditions of the study are explored. For this reason, new experiments must be conducted by researchers that have interest in analyzing gene expression of treatments and/or conditions present, but not explored, in these studies. Here, we present the RGeasy tool, which aims to facilitate the selection of reference genes, allowing the user to choose genes for a greater number of combinations of treatments/conditions, compared to the ones present in the original articles, through just a few clicks. RGeasy was validated with RT-qPCR data from gene expression studies performed in two coffee species, Coffea arabica and Coffea canephora, and it can be used for any animal, plant or microorganism species. In addition to displaying a rank of the most stable reference genes for each condition or treatment, the user also has access to the primer pairs for the selected reference genes.

Selecting reference genes for RT-qPCR studies involving the presence of B chromosomes in Psalidodon (Characiformes, Characidae).

BACKGROUND: B chromosomes are extra non-essential elements present in several eukaryotes. Unlike A chromosomes which are essential and present in all individuals of a species, B chromosomes are not necessary for normal functioning of an organism. Formerly regarded as genetically inactive, B chromosomes have been discovered to not only express their own genes, but also to exert influence on gene expression in A chromosomes. Recent studies have shown that, in some Psalidodon (Characiformes, Characidae) species, B chromosomes might be associated with phenotypic effects, such as changes in the reproductive cycle and gene expression. METHODS AND RESULTS: In this study, we aimed to establish stable reference genes for RT-qPCR experiments conducted on gonads of three fish species within Psalidodon genus, both in the presence and absence of B chromosomes. The stability of five selected reference genes was assessed using NormFinder, geNorm, BestKeeper, and RefFinder algorithms. We determined ppiaa and pgk1 as the most stable genes in P. fasciatus, whereas ppiaa and hmbsa showed the highest stability in P. bockmanni. For P. paranae, tbp and hprt1 were the most stable genes in females, and ppiaa and hprt1 were the most stable in males. CONCLUSIONS: We determined the most stable reference genes in gonads of three Psalidodon species considering the presence of B chromosomes. This is the first report of reference gene stability in the genus and provides valuable tools to better understand the effects of B chromosomes at gene expression level.

A systematic review on the selection of reference genes for gene expression studies in rodents: are the classics the best choice?

Rodents are commonly used as animal models in studies investigating various experimental conditions, often requiring gene expression analysis. Quantitative real-time reverse transcription PCR (RT-qPCR) is the most widely used tool to quantify target gene expression levels under different experimental conditions in various biological samples. Relative normalization with reference genes is a crucial step in RT-qPCR to obtain reliable quantification results. In this work, the main reference genes used in gene expression studies among the three rodents commonly employed in scientific research-hamster, rat, and mouse-are analyzed and described. An individual literature search for each rodent was conducted using specific search terms in three databases: PubMed, Scopus, and Web of Science. A total of 157 articles were selected (rats = 73, mice = 79, and hamsters = 5), identifying various reference genes. The most commonly used reference genes were analyzed according to each rodent, sample type, and experimental condition evaluated, revealing a great variability in the stability of each gene across different samples and conditions. Classic genes, which are expected to be stably expressed in both samples and conditions analyzed, demonstrated greater variability, corroborating existing concerns about the use of these genes. Therefore, this review provides important insights for researchers seeking to identify suitable reference genes for their validation studies in rodents.

Development and evaluation of suitable reference genes for qRT-PCR normalization of hybrids derived from Lycium barbarum and Lycium ruthenicum.

BACKGROUND: A correct and stably expressing reference gene is prerequisite for successful quantitative real-time PCR (qRT-PCR). Investigating gene expression profiling during flower development could enhance our understanding of the molecular mechanisms of flower formation and fertility in Lycium. METHODS AND RESULTS: In this study, 11 candidate reference genes in Lycium flower development were selected from transcriptome sequence data and evaluated with five traditional housekeeping genes from previous studies based on qRT-PCR amplification. Comparing the expression stability result of 16 candidate genes using GeNorm, NormFinder, BestKeeper, and Delta Ct algorithms, Lba04g01649 and Lba12g02820 were validated as the optimal reference genes for the flower development of Lycium. CONCLUSIONS: The reference genes identified in this study would improve the accuracy of qRT-PCR quantification of target gene expression in Lycium flower development and facilitate future functional genomics studies on flower development. This research could lay the foundation for the study of the reproduction and development of the Lycium flower.

scMRMA: single cell multiresolution marker-based annotation.

Single-cell RNA sequencing has become a powerful tool for identifying and characterizing cellular heterogeneity. One essential step to understanding cellular heterogeneity is determining cell identities. The widely used strategy predicts identities by projecting cells or cell clusters unidirectionally against a reference to find the best match. Here, we develop a bidirectional method, scMRMA, where a hierarchical reference guides iterative clustering and deep annotation with enhanced resolutions. Taking full advantage of the reference, scMRMA greatly improves the annotation accuracy. scMRMA achieved better performance than existing methods in four benchmark datasets and successfully revealed the expansion of CD8 T cell populations in squamous cell carcinoma after anti-PD-1 treatment.

Identification of Reliable Reference Genes for Use in Gene Expression Studies in Rat Febrile Seizure Model.

The study of the pathogenesis of febrile seizures and their consequences frequently necessitates gene expression analysis. The primary methodology employed for such analysis is reverse transcription with quantitative polymerase chain reaction (RT-qPCR). To ensure the accuracy of data obtained by RT-qPCR, it is crucial to utilize stably expressed reference genes. The objective of this study was to identify the most suitable reference genes for use in the analysis of mRNA production in various brain regions of rats following prolonged neonatal febrile seizures. The expression stability of eight housekeeping genes was evaluated using the online tool RefFinder in the dorsal and ventral hippocampal regions and in the temporal and medial prefrontal cortex of the brain. The Ppia gene exhibited the greatest stability of expression. Conversely, the genes with the least stable expression levels were Actb and Ywhaz; thus, it is not recommended to use them for normalization in a febrile seizure model. Additionally, the majority of housekeeping genes demonstrate age-related, region-specific fluctuations. Therefore, it is crucial to employ the appropriate housekeeping genes for each brain structure under investigation when examining the expression dynamics of genes of interest in a febrile seizure model.

Copolymerization of single-cell nucleic acids into balls of acrylamide gel.

We show the use of 5'-Acrydite oligonucleotides to copolymerize single-cell DNA or RNA into balls of acrylamide gel (BAGs). Combining this step with split-and-pool techniques for creating barcodes yields a method with advantages in cost and scalability, depth of coverage, ease of operation, minimal cross-contamination, and efficient use of samples. We perform DNA copy number profiling on mixtures of cell lines, nuclei from frozen prostate tumors, and biopsy washes. As applied to RNA, the method has high capture efficiency of transcripts and sufficient consistency to clearly distinguish the expression patterns of cell lines and individual nuclei from neurons dissected from the mouse brain. By using varietal tags (UMIs) to achieve sequence error correction, we show extremely low levels of cross-contamination by tracking source-specific SNVs. The method is readily modifiable, and we will discuss its adaptability and diverse applications.

Prediction of tumor purity from gene expression data using machine learning.

MOTIVATION: Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biological interpretation of the results, and thus, accurate prediction of tumor purity is critical. Although several methods have been proposed to predict tumor purity using high-throughput molecular data, there has been no comprehensive study on machine learning-based methods for the estimation of tumor purity. RESULTS: We applied various machine learning models to estimate tumor purity. Overall, the models predicted the tumor purity accurately and showed a high correlation with well-established gold standard methods. In addition, we identified a small group of genes and demonstrated that they could predict tumor purity well. Finally, we confirmed that these genes were mainly involved in the immune system. AVAILABILITY: The machine learning models constructed for this study are available at https://github.com/BonilKoo/ML_purity.

Toward a gold standard for benchmarking gene set enrichment analysis.

MOTIVATION: Although gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected datasets and biological reasoning on the relevance of resulting enriched gene sets. RESULTS: We develop an extensible framework for reproducible benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization and detection of relevant processes. This framework incorporates a curated compendium of 75 expression datasets investigating 42 human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods, identifying significant differences in runtime and applicability to RNA-seq data, fraction of enriched gene sets depending on the null hypothesis tested and recovery of the predefined relevance rankings. We make practical recommendations on how methods originally developed for microarray data can efficiently be applied to RNA-seq data, how to interpret results depending on the type of gene set test conducted and which methods are best suited to effectively prioritize gene sets with high phenotype relevance. AVAILABILITY: http://bioconductor.org/packages/GSEABenchmarkeR. CONTACT: ludwig.geistlinger@sph.cuny.edu.

On the cross-population generalizability of gene expression prediction models.

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.

Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells.

The stochasticity of gene expression presents significant challenges to the modeling of genetic networks. A two-state model describing promoter switching, transcription, and messenger RNA (mRNA) decay is the standard model of stochastic mRNA dynamics in eukaryotic cells. Here, we extend this model to include mRNA maturation, cell division, gene replication, dosage compensation, and growth-dependent transcription. We derive expressions for the time-dependent distributions of nascent mRNA and mature mRNA numbers, provided two assumptions hold: 1) nascent mRNA dynamics are much faster than those of mature mRNA; and 2) gene-inactivation events occur far more frequently than gene-activation events. We confirm that thousands of eukaryotic genes satisfy these assumptions by using data from yeast, mouse, and human cells. We use the expressions to perform a sensitivity analysis of the coefficient of variation of mRNA fluctuations averaged over the cell cycle, for a large number of genes in mouse embryonic stem cells, identifying degradation and gene-activation rates as the most sensitive parameters. Furthermore, it is shown that, despite the model's complexity, the time-dependent distributions predicted by our model are generally well approximated by the negative binomial distribution. Finally, we extend our model to include translation, protein decay, and auto-regulatory feedback, and derive expressions for the approximate time-dependent protein-number distributions, assuming slow protein decay. Our expressions enable us to study how complex biological processes contribute to the fluctuations of gene products in eukaryotic cells, as well as allowing a detailed quantitative comparison with experimental data via maximum-likelihood methods.

Transcriptomic signatures of tumors undergoing T cell attack.

BACKGROUND: Studying tumor cell-T cell interactions in the tumor microenvironment (TME) can elucidate tumor immune escape mechanisms and help predict responses to cancer immunotherapy. METHODS: We selected 14 pairs of highly tumor-reactive tumor-infiltrating lymphocytes (TILs) and autologous short-term cultured cell lines, covering four distinct tumor types, and co-cultured TILs and tumors at sub-lethal ratios in vitro to mimic the interactions occurring in the TME. We extracted gene signatures associated with a tumor-directed T cell attack based on transcriptomic data of tumor cells. RESULTS: An autologous T cell attack induced pronounced transcriptomic changes in the attacked tumor cells, partially independent of IFN-γ signaling. Transcriptomic changes were mostly independent of the tumor histological type and allowed identifying common gene expression changes, including a shared gene set of 55 transcripts influenced by T cell recognition (Tumors undergoing T cell attack, or TuTack, focused gene set). TuTack scores, calculated from tumor biopsies, predicted the clinical outcome after anti-PD-1/anti-PD-L1 therapy in multiple tumor histologies. Notably, the TuTack scores did not correlate to the tumor mutational burden, indicating that these two biomarkers measure distinct biological phenomena. CONCLUSIONS: The TuTack scores measure the effects on tumor cells of an anti-tumor immune response and represent a comprehensive method to identify immunologically responsive tumors. Our findings suggest that TuTack may allow patient selection in immunotherapy clinical trials and warrant its application in multimodal biomarker strategies.

Screening and stability analysis of reference genes in fasting caecotrophy model in rabbits.

BACKGROUND: The selection and validation of stably expressed reference genes is key for accurately quantifying the mRNA abundance of genes under different treatments. In the rabbit model of fasting caecotrophy, reports about the selection of stable reference genes are not available. METHODS AND RESULTS: This study aims to screen suitable reference genes in different tissues (including uterus, cecum, and liver) of rabbits between control and fasting caecotrophy groups. RT-qPCR was used to analyze the expression levels of eight commonly used reference genes (including GAPDH, 18S rRNA, B2M, CYP, HPRT1, ß-actin, H2afz, Ywhaz), and RefFinder (including geNorm, NormFinder, and BestKeeper) was used to analyze the expression stability of these reference genes. Our results showed that the most stable reference genes were different in different tissues and treatments. In the control and fasting caecotrophy groups, CYP, GAPDH and HPRT1 were proven to be the top stable reference genes in the uterus, cecum, and liver tissues, respectively. GAPDH and Ywhaz were proven to be the top two stable reference genes among uterus, cecum, and liver in both control and fasting caecotrophy groups. CONCLUSIONS: Our results indicated that the combined analysis of three or more reference genes (GAPDH, HPRT1, and Ywhaz) are recommended to be used for RT-qPCR normalization in the rabbit model of fasting caecotrophy, and that GAPDH is a better choice than the other reference genes for normalizing the relative expression of target genes in different tissues of fasting caecotrophy rabbits.

Robust ΔΔct estimate.

The ΔΔct method estimates fold change in gene expression data from RT-PCR assay. The ΔΔct estimate aggregates replicates using mean and standard deviation (sd) and is not robust to outliers which are in practice often removed before the non-outlying replicates are aggregated. The alternative of using robust statistics such as median and median absolute deviation (MAD) to aggregate the replicates is not done in practice perhaps because the distribution of a robust ΔΔct estimate based on median and MAD is not straightforward to deduce. We introduce a robust ΔΔct estimate and deduce an approximate distribution for it. Simulations show that when data has outliers, the robust ΔΔct estimate compared to the non-robust ΔΔct estimate leads to significantly reduced confidence interval length and a coverage close to the nominal coverage. The analysis of an RT-PCR data from a Novartis clinical trial demonstrates benefit of a robust ΔΔct estimate.

Short tandem repeat profiling for the authentication of cancer stem-like cells.

Colorectal and glioblastoma cancer stem-like cells (CSCs) are essential for translational research. Cell line authentication by short tandem repeat (STR) profiling ensures reproducibility of results in oncology research. This technique enables to identify mislabeling or cross-contamination of cell lines. In our study, we provide a reference dataset for a panel of colorectal and glioblastoma CSCs that allows authentication. Each cell line was entered into the cell Line Integrated Molecular Authentication database 2.1 to be compared to the STR profiles of 4485 tumor cell lines. This article also provides clinical data of patients from whom CSCs arose and data on the parent tumor stage and mutations. STR profiles and information of our CSCs are also available in the Cellosaurus database (ExPASy) as identified by unique research resource identifier codes.

Appropriate use of cancer comprehensive genome profiling assay using circulating tumor DNA.

Comprehensive genomic profiling (CGP) is being increasingly used for the routine clinical management of solid cancers. In July 2018, the use of tumor tissue-based CGP assays became available for all solid cancers under the universal health insurance system in Japan. Several restrictions presently exist, such as patient eligibility and limitations on the opportunities to perform such assays. The clinical implementation of CGP based on plasma circulating tumor DNA (ctDNA) is also expected to raise issues regarding the selection and use of tissue DNA and ctDNA CGP. A Joint Task Force for the Promotion of Cancer Genome Medicine comprised of three Japanese cancer-related societies has formulated a policy proposal for the appropriate use of plasma CGP (in Japanese), available at https://www.jca.gr.jp/researcher/topics/2021/files/20210120.pdf, http://www.jsco.or.jp/jpn/user_data/upload/File/20210120.pdf, and https://www.jsmo.or.jp/file/dl/newsj/2765.pdf. Based on these recommendations, the working group has summarized the respective advantages and cautions regarding the use of tissue DNA CGP and ctDNA CGP with reference to the advice of a multidisciplinary expert panel, the preferred use of plasma specimens over tissue, and multiple ctDNA testing. These recommendations have been prepared to maximize the benefits of performing CGP assays and might be applicable in other countries and regions.