NASC-seq monitors RNA synthesis in single cells

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Shining a light on cell biology of the nucleus with single-cell sequencing

From the preservation of genomic integrity to the regulation of RNA translation, nearly all cellular processes are regulated in a cell context-dependent manner. To fully understand the context-specific function of involved nuclear processes, a vast number of single-cell sequencing technologies were developed over the last decade. This instrumental work demonstrated the heterogeneity between cell types and individual cells, bringing about new understanding of nuclear mechanisms and their crosstalk to cell states. In this review, we will cover new technological advances and their exciting applications as well as future opportunities to discover new nuclear processes and the crosstalk between them.

A convenient single-cell newly synthesized transcriptome assay reveals FLI1 downregulation during T-cell activation

Sequencing newly synthesized transcriptome alongside regular transcriptome in single cells enables the study of gene expression temporal dynamics during rapid chromatin and gene regulation processes. Existing assays for profiling single-cell newly synthesized transcriptome often require specialized technical expertise to achieve high cellular throughput, limiting their accessibility. Here, we developed NOTE-seq, a method for simultaneous profiling of regular and newly synthesized transcriptomes in single cells with high cellular throughput. NOTE-seq integrates 4-thiouridine labeling of newly synthesized RNA, thiol-alkylation-based chemical conversion, and a streamlined 10X Genomics workflow, making it accessible and convenient for biologists without extensive single-cell expertise. Using NOTE-seq, we investigated the temporal dynamics of gene expression during early-stage T-cell activation, identified transcription factors and regulons in Jurkat and naïve T cells, and uncovered the down-regulation of FLI1 as a master transcription factor upon T-cell stimulation. Notably, topoisomerase inhibition led to the depletion of both topoisomerases and FLI1 in T cells through a proteasome-dependent mechanism driven by topoisomerase cleavage complexes, highlighting potential complications topoisomerase-targeting cancer chemotherapies could pose to the immune system.

Integrating Metabolic RNA Labeling-Based Time-Resolved Single-Cell RNA Sequencing with Spatial Transcriptomics for Spatiotemporal Transcriptomic Analysis

Metabolic RNA labeling-based time-resolved single-cell RNA sequencing (scRNA-seq) has provided unprecedented tools to dissect the temporal dynamics and the complex gene regulatory networks of gene expression. However, this technology fails to reveal the spatial organization of cells in tissues, which also regulates the gene expression by intercellular communication. Herein, it is demonstrated that integrating time-resolved scRNA-seq with spatial transcriptomics is a new paradigm for spatiotemporal analysis. Metabolic RNA labeling-based time-resolved Well-TEMP-seq is first applied to profile the transcriptional dynamics of glioblastoma (GBM) cells and discover two potential pathways of EZH2-mediated mesenchymal transition in GBM. With spatial transcriptomics, it is further revealed that the crosstalk between CCL2+ malignant cells and IL10+ tumor-associated macrophages in the tumor microenvironment through an EZH2-FOSL2-CCL2 axis contributes to the mesenchymal transition in GBM. These discoveries show the power of integrative spatiotemporal scRNA-seq to elucidate the complex gene regulatory mechanism and advance the understanding of cellular processes in disease.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Targeted barcoding of variable antibody domains and individual transcriptomes of the human B-cell repertoire using Link-Seq

Here, we present Link-Seq, a highly efficient droplet microfluidic method for combined sequencing of antibody-encoding genes and the transcriptome of individual B cells at large scale. The method is based on 3' barcoding of the transcriptome and subsequent single-molecule PCR in droplets, which freely shift the barcode along specific gene regions, such as the antibody heavy- and light-chain genes. Using the immune repertoire of COVID-19 patients and healthy donors as a model system, we obtain up to 91.7% correctly paired immunoglobulin heavy and light chains. Furthermore, we map the V(D)J usage and obtain sensitivities comparable with the current gold-standard 10× Genomics commercial systems while offering full flexibility in experimental setup and significant cost savings. A further unique feature of Link-Seq is the possibility of barcoding multiple target genes in a site-specific manner. Based on the open character of the platform and its conceptual advantages, we expect Link-Seq to become a versatile tool for single-cell analysis, especially for applications requiring additional processing steps that cannot be implemented on commercially available platforms.

Quantitative Characterization of Gene Regulatory Circuits Associated With Fungal Secondary Metabolism to Discover Novel Natural Products

Microbial genetic circuits are vital for regulating gene expression and synthesizing bioactive compounds. However, assessing their strength and timing, especially in multicellular fungi, remains challenging. Here, an advanced microfluidic platform is combined with a mathematical model enabling precise characterization of fungal gene regulatory circuits (GRCs) at the single-cell level. Utilizing this platform, the expression intensity and timing of 30 transcription factor-promoter combinations derived from two representative fungal GRCs, using the model fungus Aspergillus nidulans are determined. As a proof of concept, the selected GRC combination is utilized to successfully refactor the biosynthetic pathways of bioactive molecules, precisely control their production, and activate the expression of the silenced biosynthetic gene clusters (BGCs). This study provides insights into microbial gene regulation and highlights the potential of platform in fungal synthetic biology applications and the discovery of novel natural products.

Transcriptome and Temporal Transcriptome Analyses in Single Cells

Transcriptome analysis in single cells, enabled by single-cell RNA sequencing, has become a prevalent approach in biomedical research, ranging from investigations of gene regulation to the characterization of tissue organization. Over the past decade, advances in single-cell RNA sequencing technology, including its underlying chemistry, have significantly enhanced its performance, marking notable improvements in methodology. A recent development in the field, which integrates RNA metabolic labeling with single-cell RNA sequencing, has enabled the profiling of temporal transcriptomes in individual cells, offering new insights into dynamic biological processes involving RNA kinetics and cell fate determination. In this review, we explore the chemical principles and design improvements that have enhanced single-molecule capture efficiency, improved RNA quantification accuracy, and increased cellular throughput in single-cell transcriptome analysis. We also illustrate the concept of RNA metabolic labeling for detecting newly synthesized transcripts and summarize recent advancements that enable single-cell temporal transcriptome analysis. Additionally, we examine data analysis strategies for the precise quantification of newly synthesized transcripts and highlight key applications of transcriptome and temporal transcriptome analyses in single cells.

Time-resolved single-cell transcriptomic sequencing

Cells experience continuous transformation under both physiological and pathological circumstances. Single-cell RNA sequencing (scRNA-seq) is competent in disclosing the disparities of cells; nevertheless, it poses challenges in linking the individual cell state at distinct time points. Although computational approaches based on scRNA-seq data have been put forward for trajectory analysis, the result is based on assumptions and fails to reflect the actual states. Consequently, it is necessary to incorporate a "time anchor" into the scRNA-seq library for the temporal documentation of the dynamic expression pattern. This review comprehensively overviews the time-resolved single-cell transcriptomic sequencing methodologies and applications. As scRNA-seq functions as the basis for profiling single-cell expression patterns, the review initially introduces various scRNA-seq approaches. Subsequently, the review focuses on the different experimental strategies for introducing a "time anchor" to scRNA-seq, highlighting their principles, strengths, weaknesses, and comparing their adaptation in various scenarios. Next, it provides a brief summary of applications in immunity response, cancer progression, and embryo development. Finally, the review concludes with a forward-looking perspective on future advancements in time-resolved single-cell transcriptomic sequencing.

Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information

The time-resolved scRNA-seq (tscRNA-seq) provides the possibility to infer physically meaningful kinetic parameters, e.g., the transcription, splicing or RNA degradation rate constants with correct magnitudes, and RNA velocities by incorporating temporal information. Previous approaches utilizing the deterministic dynamics and steady-state assumption on gene expression states are insufficient to achieve favorable results for the data involving transient process. We present a dynamical approach, Storm (Stochastic models of RNA metabolic-labeling), to overcome these limitations by solving stochastic differential equations of gene expression dynamics. The derivation reveals that the new mRNA sequencing data obeys different types of cell-specific Poisson distributions when jointly considering both biological and cell-specific technical noise. Storm deals with measured counts data directly and extends the RNA velocity methodology based on metabolic labeling scRNA-seq data to transient stochastic systems. Furthermore, we relax the constant parameter assumption over genes/cells to obtain gene-cell-specific transcription/splicing rates and gene-specific degradation rates, thus revealing time-dependent and cell-state-specific transcriptional regulations. Storm will facilitate the study of the statistical properties of tscRNA-seq data, eventually advancing our understanding of the dynamic transcription regulation during development and disease.

Dyna-vivo-seq unveils cellular RNA dynamics during acute kidney injury via in vivo metabolic RNA labeling-based scRNA-seq

A fundamental objective of genomics is to track variations in gene expression program. While metabolic RNA labeling-based single-cell RNA sequencing offers insights into temporal biological processes, its limited applicability only to in vitro models challenges the study of in vivo gene expression dynamics. Herein, we introduce Dyna-vivo-seq, a strategy that enables time-resolved dynamic transcription profiling in vivo at the single-cell level by examining new and old RNAs. The new RNAs can offer an additional dimension to reveal cellular heterogeneity. Leveraging new RNAs, we discern two distinct high and low metabolic labeling populations among proximal tubular (PT) cells. Furthermore, we identify 90 rapidly responding transcription factors during the acute kidney injury in female mice, highlighting that high metabolic labeling PT cells exhibit heightened susceptibility to injury. Dyna-vivo-seq provides a powerful tool for the characterization of dynamic transcriptome at the single-cell level in living organism and holds great promise for biomedical applications.

Expanding and improving analyses of nucleotide recoding RNA-seq experiments with the EZbakR suite

Nucleotide recoding RNA sequencing methods (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) are powerful approaches for assaying transcript population dynamics. In addition, these methods have been extended to probe a host of regulated steps in the RNA life cycle. Current bioinformatic tools significantly constrain analyses of NR-seq data. To address this limitation, we developed EZbakR, an R package to facilitate a more comprehensive set of NR-seq analyses, and fastq2EZbakR, a Snakemake pipeline for flexible preprocessing of NR-seq datasets, collectively referred to as the EZbakR suite. Together, these tools generalize many aspects of the NR-seq analysis workflow. The fastq2EZbakR pipeline can assign reads to a diverse set of genomic features (e.g., genes, exons, splice junctions, etc.), and EZbakR can perform analyses on any combination of these features. EZbakR extends standard NR-seq mutational modeling to support multi-label analyses (e.g., s4U and s6G dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. EZbakR also generalizes dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, EZbakR implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling. The EZbakR suite will thus allow researchers to make full, effective use of NR-seq data.

Single-cell new RNA sequencing reveals principles of transcription at the resolution of individual bursts

Analyses of transcriptional bursting from single-cell RNA-sequencing data have revealed patterns of variation and regulation in the kinetic parameters that could be inferred. Here we profiled newly transcribed (4-thiouridine-labelled) RNA across 10,000 individual primary mouse fibroblasts to more broadly infer bursting kinetics and coordination. We demonstrate that inference from new RNA profiles could separate the kinetic parameters that together specify the burst size, and that the synthesis rate (and not the transcriptional off rate) controls the burst size. Importantly, transcriptome-wide inference of transcriptional on and off rates provided conclusive evidence that RNA polymerase II transcribes genes in bursts. Recent reports identified examples of transcriptional co-bursting, yet no global analyses have been performed. The deep new RNA profiles we generated with allelic resolution demonstrated that co-bursting rarely appears more frequently than expected by chance, except for certain gene pairs, notably paralogues located in close genomic proximity. Altogether, new RNA single-cell profiling critically improves the inference of transcriptional bursting and provides strong evidence for independent transcriptional bursting of mammalian genes.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA sequencing

Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.

Dissecting key regulators of transcriptome kinetics through scalable single-cell RNA profiling of pooled CRISPR screens

We present a combinatorial indexing method, PerturbSci-Kinetics, for capturing whole transcriptomes, nascent transcriptomes and single guide RNA (sgRNA) identities across hundreds of genetic perturbations at the single-cell level. Profiling a pooled CRISPR screen targeting various biological processes, we show the gene expression regulation during RNA synthesis, processing and degradation, miRNA biogenesis and mitochondrial mRNA processing, systematically decoding the genome-wide regulatory network that underlies RNA temporal dynamics at scale.

Stromal Rigidity Stress Accelerates Pancreatic Intraepithelial Neoplasia Progression and Chromosomal Instability via Nuclear Protein Tyrosine Kinase 2 Localization

Because the mechanotransduction by stromal stiffness stimulates the rupture and repair of the nuclear envelope in pancreatic progenitor cells, accumulated genomic aberrations are under selection in the tumor microenvironment. Analysis of cell growth, micronuclei, and phosphorylated Ser-139 residue of the histone variant H2AX (γH2AX) foci linked to mechanotransduction pressure in vivo during serial orthotopic passages of mouse KrasLSL-G12D/+;Trp53flox/flox;Pdx1-Cre (KPC) cancer cells in the tumor and in migrating through the size-restricted 3-μm micropores. To search for pancreatic cancer cell-of-origin, analysis of single-cell data sets revealed that the extracellular matrix shaped an alternate route of acinar-ductal transdifferentiation of acinar cells into topoisomerase II α (TOP2A)-overexpressing cancer cells and derived subclusters with copy number amplifications in MYC-PTK2 (protein tyrosine kinase 2) locus and PIK3CA. High-PTK2 expression is associated with 171 differentially methylated CpG loci, 319 differentially expressed genes, and poor overall survival in The Cancer Genome Atlas-Pancreatic Adenocarcinoma cohort. Abolished RGD-integrin signaling by disintegrin KG blocked the PTK2 phosphorylation, increased cancer apoptosis, decreased vav guanine nucleotide exchange factor 1 (VAV1) expression, and prolonged overall survival in the KPC mice. Reduction of α-smooth muscle actin deposition in the CD248 knockout KPC mice remodeled the tissue stroma and down-regulated TOP2A expression in the epithelium. In summary, stromal stiffness induced the onset of cancer cells-of-origin by ectopic TOP2A expression, and the genomic amplification of MYC-PTK2 locus via alternative transdifferentiation of pancreatic progenitor cells is the vulnerability useful for disintegrin KG treatment.

CellRank 2: unified fate mapping in multiview single-cell data

Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.

Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics

The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.

A dynamical perspective: moving towards mechanism in single-cell transcriptomics

As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Imprinted X chromosome inactivation at the gamete-to-embryo transition

In mammals, dosage compensation involves two parallel processes: (1) X inactivation, which equalizes X chromosome dosage between males and females, and (2) X hyperactivation, which upregulates the active X for X-autosome balance. The field currently favors models whereby dosage compensation initiates "de novo" during mouse development. Here, we develop "So-Smart-seq" to revisit the question and interrogate a comprehensive transcriptome including noncoding genes and repeats in mice. Intriguingly, de novo silencing pertains only to a subset of Xp genes. Evolutionarily older genes and repetitive elements demonstrate constitutive Xp silencing, adopt distinct signatures, and do not require Xist to initiate silencing. We trace Xp silencing backward in developmental time to meiotic sex chromosome inactivation in the male germ line and observe that Xm hyperactivation is timed to Xp silencing on a gene-by-gene basis. Thus, during the gamete-to-embryo transition, older Xp genes are transmitted in a "pre-inactivated" state. These findings have implications for the evolution of imprinting.

Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq

During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities. Here, we combine single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell types. Our study provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.

Resolving cellular dynamics using single-cell temporal transcriptomics

Cellular dynamics, the transition of a cell from one state to another, is central to understanding developmental processes and disease progression. Single-cell transcriptomics has been pushing the frontiers of cellular dynamics studies into a genome-wide and single-cell level. While most single-cell RNA sequencing approaches are disruptive and only provide a snapshot of cell states, the dynamics of a cell could be reconstructed by either exploiting temporal information hiding in the transcriptomics data or integrating additional information. In this review, we describe these approaches, highlighting their underlying principles, key assumptions, and the rationality to interpret the results as models. We also discuss the recently emerging nondisruptive live-cell transcriptomics methods, which are highly complementary to the computational models for their assumption-free nature.

SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle

RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time‐dependent kinetic rates during dynamic processes. Here, we present SLAM‐Drop‐seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol‐fixed cells with droplet‐based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene‐specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust‐cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM‐Drop‐seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single‐cell gene expression dynamics in continuous biological processes.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Transcriptomic forecasting with neural ordinary differential equations

Single-cell transcriptomics technologies can uncover changes in the molecular states that underlie cellular phenotypes. However, understanding the dynamic cellular processes requires extending from inferring trajectories from snapshots of cellular states to estimating temporal changes in cellular gene expression. To address this challenge, we have developed a neural ordinary differential-equation-based method, RNAForecaster, for predicting gene expression states in single cells for multiple future time steps in an embedding-independent manner. We demonstrate that RNAForecaster can accurately predict future expression states in simulated single-cell transcriptomic data with cellular tracking over time. We then show that by using metabolic labeling single-cell RNA sequencing (scRNA-seq) data from constitutively dividing cells, RNAForecaster accurately recapitulates many of the expected changes in gene expression during progression through the cell cycle over a 3-day period. Thus, RNAForecaster enables short-term estimation of future expression states in biological systems from high-throughput datasets with temporal information.

Understanding tumour endothelial cell heterogeneity and function from single-cell omics

Anti-angiogenic therapies (AATs) are used to treat different types of cancers. However, their success is limited owing to insufficient efficacy and resistance. Recently, single-cell omics studies of tumour endothelial cells (TECs) have provided new mechanistic insight. Here, we overview the heterogeneity of human TECs of all tumour types studied to date, at the single-cell level. Notably, most human tumour types contain varying numbers but only a small population of angiogenic TECs, the presumed targets of AATs, possibly contributing to the limited efficacy of and resistance to AATs. In general, TECs are heterogeneous within and across all tumour types, but comparing TEC phenotypes across tumours is currently challenging, owing to the lack of a uniform nomenclature for endothelial cells and consistent single-cell analysis protocols, urgently raising the need for a more consistent approach. Nonetheless, across most tumour types, universal TEC markers (ACKR1, PLVAP and IGFBP3) can be identified. Besides angiogenesis, biological processes such as immunomodulation and extracellular matrix organization are among the most commonly predicted enriched signatures of TECs across different tumour types. Although angiogenesis and extracellular matrix targets have been considered for AAT (without the hoped success), the immunomodulatory properties of TECs have not been fully considered as a novel anticancer therapeutic approach. Therefore, we also discuss progress, limitations, solutions and novel targets for AAT development.

Synergising single-cell resolution and 4sU labelling boosts inference of transcriptional bursting

Despite the recent rise of RNA-seq datasets combining single-cell (sc) resolution with 4-thiouridine (4sU) labelling, analytical methods exploiting their power to dissect transcriptional bursting are lacking. Here, we present a mathematical model and Bayesian inference implementation to facilitate genome-wide joint parameter estimation and confidence quantification (R package: burstMCMC). We demonstrate that, unlike conventional scRNA-seq, 4sU scRNA-seq resolves temporal parameters and furthermore boosts inference of dimensionless parameters via a synergy between single-cell resolution and 4sU labelling. We apply our method to published 4sU scRNA-seq data and linked with ChIP-seq data, we uncover previously obscured associations between different parameters and histone modifications.

grandR: a comprehensive package for nucleotide conversion RNA-seq data analysis

Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. Here we present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.

PerturbSci-Kinetics: Dissecting key regulators of transcriptome kinetics through scalable single-cell RNA profiling of pooled CRISPR screens

Here we described PerturbSci-Kinetics, a novel combinatorial indexing method for capturing three-layer single-cell readout (i.e., whole transcriptomes, nascent transcriptomes, sgRNA identities) across hundreds of genetic perturbations. Through PerturbSci-Kinetics profiling of pooled CRISPR screens targeting a variety of biological processes, we were able to decipher the complexity of RNA regulations at multiple levels (e.g., synthesis, processing, degradation), and revealed key regulators involved in miRNA and mitochondrial RNA processing pathways. Our technique opens the possibility of systematically decoding the genome-wide regulatory network underlying RNA temporal dynamics at scale and cost-effectively.

Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape

Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function. However, the intricate subcellular dynamics of RNA remain obscured due to the limitations of existing transcriptomics methods. Here, we report TEMPOmap-a method that uncovers subcellular RNA profiles across time and space at the single-cell level. TEMPOmap integrates pulse-chase metabolic labeling with highly multiplexed three-dimensional in situ sequencing to simultaneously profile the age and location of individual RNA molecules. Using TEMPOmap, we constructed the subcellular RNA kinetic landscape in various human cells from transcription and translocation to degradation. Clustering analysis of RNA kinetic parameters across single cells revealed 'kinetic gene clusters' whose expression patterns were shaped by multistep kinetic sculpting. Importantly, these kinetic gene clusters are functionally segregated, suggesting that subcellular RNA kinetics are differentially regulated in a cell-state- and cell-type-dependent manner. Spatiotemporally resolved transcriptomics provides a gateway to uncovering new spatiotemporal gene regulation principles.

Single-cell temporal dynamics reveals the relative contributions of transcription and degradation to cell-type specific gene expression in zebrafish embryos

During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the underlying regulation of mRNA transcription and degradation remains a challenge, especially within whole embryos with diverse cellular identities. Here, we collect temporal cellular transcriptomes of zebrafish embryos, and decompose them into their newly-transcribed (zygotic) and pre-existing (maternal) mRNA components by combining single-cell RNA-Seq and metabolic labeling. We introduce kinetic models capable of quantifying regulatory rates of mRNA transcription and degradation within individual cell types during their specification. These reveal different regulatory rates between thousands of genes, and sometimes between cell types, that shape spatio-temporal expression patterns. Transcription drives most cell-type restricted gene expression. However, selective retention of maternal transcripts helps to define the gene expression profiles of germ cells and enveloping layer cells, two of the earliest specified cell-types. Coordination between transcription and degradation restricts expression of maternal-zygotic genes to specific cell types or times, and allows the emergence of spatio-temporal patterns when overall mRNA levels are held relatively constant. Sequence-based analysis links differences in degradation to specific sequence motifs. Our study reveals mRNA transcription and degradation events that control embryonic gene expression, and provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.

SLAM-seq reveals early transcriptomic response mechanisms upon glutamine deprivation in Chinese hamster ovary cells

Mammalian cells frequently encounter subtle perturbations during recombinant protein production. Identifying the genetic factors that govern the cellular stress response can facilitate targeted genetic engineering to obtain production cell lines that demonstrate a higher stress tolerance. To simulate nutrient stress, Chinese hamster ovary (CHO) cells were transferred into a glutamine(Q)-free medium and transcriptional dynamics using thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM-seq) along with standard RNA-seq of stressed and unstressed cells were investigated. The SLAM-seq method allows differentiation between actively transcribed, nascent mRNA, and total (previously present) mRNA in the sample, adding an additional, time-resolved layer to classic RNA-sequencing. The cells tackle amino acid (AA) limitation by inducing the integrated stress response (ISR) signaling pathway, reflected in Atf4 overexpression in the early hours post Q deprivation, leading to subsequent activation of its targets, Asns, Atf3, Ddit3, Eif4ebp1, Gpt2, Herpud1, Slc7a1, Slc7a11, Slc38a2, Trib3, and Vegfa. The GCN2-eIF2α-ATF4 pathway is confirmed by a significant halt in transcription of translation-related genes at 24 h post Q deprivation. The downregulation of lipid synthesis indicates the inhibition of the mTOR pathway, further confirmed by overexpression of Sesn2. Furthermore, SLAM-seq detects short-lived transcription factors, such as Egr1, that would have been missed in standard experimental designs with RNA-seq. Our results describe the successful establishment of SLAM-seq in CHO cells and therefore facilitate its future use in other scenarios where dynamic transcriptome profiling in CHO cells is essential.

Well-TEMP-seq as a microwell-based strategy for massively parallel profiling of single-cell temporal RNA dynamics

Single-cell RNA sequencing (scRNA-seq) reveals the transcriptional heterogeneity of cells, but the static snapshots fail to reveal the time-resolved dynamics of transcription. Herein, we develop Well-TEMP-seq, a high-throughput, cost-effective, accurate, and efficient method for massively parallel profiling the temporal dynamics of single-cell gene expression. Well-TEMP-seq combines metabolic RNA labeling with scRNA-seq method Well-paired-seq to distinguish newly transcribed RNAs marked by T-to-C substitutions from pre-existing RNAs in each of thousands of single cells. The Well-paired-seq chip ensures a high single cell/barcoded bead pairing rate (~80%) and the improved alkylation chemistry on beads greatly alleviates chemical conversion-induced cell loss (~67.5% recovery). We further apply Well-TEMP-seq to profile the transcriptional dynamics of colorectal cancer cells exposed to 5-AZA-CdR, a DNA-demethylating drug. Well-TEMP-seq unbiasedly captures the RNA dynamics and outperforms the splicing-based RNA velocity method. We anticipate that Well-TEMP-seq will be broadly applicable to unveil the dynamics of single-cell gene expression in diverse biological processes.

A single-cell RNA labeling strategy for measuring stress response upon tissue dissociation

Tissue dissociation, a crucial step in single-cell sample preparation, can alter the transcriptional state of a sample through the intrinsic cellular stress response. Here we demonstrate a general approach for measuring transcriptional response during sample preparation. In our method, transcripts made during dissociation are labeled for later identification upon sequencing. We found general as well as cell-type-specific dissociation response programs in zebrafish larvae, and we observed sample-to-sample variation in the dissociation response of mouse cardiomyocytes despite well-controlled experimental conditions. Finally, we showed that dissociation of the mouse hippocampus can lead to the artificial activation of microglia. In summary, our approach facilitates experimental optimization of dissociation procedures as well as computational removal of transcriptional perturbation response.

Determining RNA Natural Modifications and Nucleoside Analog-Labeled Sites by a Chemical/Enzyme-Induced Base Mutation Principle

The natural chemical modifications of messenger RNA (mRNA) in living organisms have shown essential roles in both physiology and pathology. The mapping of mRNA modifications is critical for interpreting their biological functions. In another dimension, the synthesized nucleoside analogs can enable chemical labeling of cellular mRNA through a metabolic pathway, which facilitates the study of RNA dynamics in a pulse-chase manner. In this regard, the sequencing tools for mapping both natural modifications and nucleoside tags on mRNA at single base resolution are highly necessary. In this work, we review the progress of chemical sequencing technology for determining both a variety of naturally occurring base modifications mainly on mRNA and a few on transfer RNA and metabolically incorporated artificial base analogs on mRNA, and further discuss the problems and prospects in the field.

Application of nucleoside or nucleotide analogues in RNA dynamics and RNA-binding protein analysis

Cellular RNAs undergo dynamic changes during RNA biological processes, which are tightly orchestrated by RNA-binding proteins (RBPs). Yet, the investigation of RNA dynamics is hurdled by highly abundant steady-state RNAs, which make the signals of dynamic RNAs less detectable. Notably, the exert of nucleoside or nucleotide analogue-based RNA technologies has provided a remarkable platform for RNA dynamics research, revealing diverse unnoticed features in RNA metabolism. In this review, we focus on the application of two types of analogue-based RNA sequencing, antigen-/antibody- and click chemistry-based methodologies, and summarize the RNA dynamics features revealed. Moreover, we discuss emerging single-cell newly transcribed RNA sequencing methodologies based on nucleoside analogue labeling, which provides novel insights into RNA dynamics regulation at single-cell resolution. On the other hand, we also emphasize the identification of RBPs that interact with polyA, non-polyA RNAs, or newly transcribed RNAs and also their associated RNA-binding domains at genomewide level through ultraviolet crosslinking and mass spectrometry in different contexts. We anticipated that further modification and development of these analogue-based RNA and RBP capture technologies will aid in obtaining an unprecedented understanding of RNA biology. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA Methods > RNA Analyses in Cells.

Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions

Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy but in experiments, cells may have two gene copies as cells replicate their genome during the cell cycle. While it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging.

Fast and highly sensitive full-length single-cell RNA sequencing using FLASH-seq

We present FLASH-seq (FS), a full-length single-cell RNA sequencing (scRNA-seq) method with increased sensitivity and reduced hands-on time compared to Smart-seq3. The entire FS protocol can be performed in ~4.5 hours, is simple to automate and can be easily miniaturized to decrease resource consumption. The FS protocol can also use unique molecular identifiers (UMIs) for molecule counting while displaying reduced strand-invasion artifacts. FS will be especially useful for characterizing gene expression at high resolution across multiple samples.

Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology

Cells with the same genome can exist in different phenotypes and can change between distinct phenotypes when subject to specific stimuli and microenvironments. Some examples include cell differentiation during development, reprogramming for induced pluripotent stem cells and transdifferentiation, cancer metastasis and fibrosis progression. The regulation and dynamics of cell phenotypic conversion is a fundamental problem in biology, and has a long history of being studied within the formalism of dynamical systems. A main challenge for mechanism-driven modeling studies is acquiring sufficient amount of quantitative information for constraining model parameters. Advances in quantitative experimental approaches, especially high throughput single-cell techniques, have accelerated the emergence of a new direction for reconstructing the governing dynamical equations of a cellular system from quantitative single-cell data, beyond the dominant statistical approaches. Here I review a selected number of recent studies using live- and fixed-cell data and provide my perspective on future development.

Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology

Cells with the same genome can exist in different phenotypes and can change between distinct phenotypes when subject to specific stimuli and microenvironments. Some examples include cell differentiation during development, reprogramming for induced pluripotent stem cells and transdifferentiation, cancer metastasis and fibrosis progression. The regulation and dynamics of cell phenotypic conversion is a fundamental problem in biology, and has a long history of being studied within the formalism of dynamical systems. A main challenge for mechanism-driven modeling studies is acquiring sufficient amount of quantitative information for constraining model parameters. Advances in quantitative experimental approaches, especially high throughput single-cell techniques, have accelerated the emergence of a new direction for reconstructing the governing dynamical equations of a cellular system from quantitative single-cell data, beyond the dominant statistical approaches. Here I review a selected number of recent studies using live- and fixed-cell data and provide my perspective on future development.

Inferring gene regulation from stochastic transcriptional variation across single cells at steady state

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

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.

Synthesis of 4-thiouridines with prodrug functionalization for RNA metabolic labeling

Metabolic labeling has emerged as a powerful tool to endow RNA with reactive handles allowing for subsequent chemical derivatization and processing. Recently, thiolated nucleosides, such as 4-thiouridine (4sU), have attracted great interest in metabolic labeling-based RNA sequencing approaches (TUC-seq, SLAM-seq, TimeLapse-seq) to study cellular RNA expression and decay dynamics. For these and other applications (e.g. PAR-CLIP), thus far only the naked nucleoside 4sU has been applied. Here we examined the concept of derivatizing 4sU into a 5'-monophosphate prodrug that would allow for cell permeation and potentially improve labeling efficiency by bypassing the rate-limiting first step of 5' phosphorylation of the nucleoside into the ultimately bioactive 4sU triphosphate (4sUTP). To this end, we developed robust synthetic routes towards diverse 4sU monophosphate prodrugs. Using metabolic labeling assays, we found that most of the newly introduced 4sU prodrugs were well tolerated by the cells. One derivative, the bis(4-acetyloxybenzyl) 5'-monophosphate of 4sU, was also efficiently incorporated into nascent RNA.

Single-cell immunology: Past, present, and future

The immune system is a complex, dynamic, and plastic ecosystem composed of multiple cell types that constantly sense and interact with their local microenvironment to protect from infection and maintain homeostasis. For over a century, great efforts and ingenuity have been applied to the characterization of immune cells and their microenvironments, but traditional marker-based and bulk technologies left key questions unanswered. In the past decade, the advent of single-cell genomic approaches has revolutionized our knowledge of the cellular and molecular makeup of the immune system. In this perspective, we outline the past, present, and future applications of single-cell genomics in immunology and discuss how the integration of multiomics at the single-cell level will pave the way for future advances in immunology research and clinical translation.

Mapping transcriptomic vector fields of single cells

Single-cell (sc)-RNA-seq, together with RNA-velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo, that infers absolute RNA velocity, reconstructs continuous vector-field functions that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically-labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1–GATA1 circuit. Leveraging the Least-Action-Path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo thus represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Temporal modelling using single-cell transcriptomics

Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.

In Situ Visualizing Nascent RNA by Exploring DNA-Templated Oxidative Amination of 4-Thiouridine

Tracking and mapping the nascent RNA molecules in cells is essential for deciphering embryonic development and neuronal differentiation. Here, we utilized 4-thiouridine (s4U) as a metabolic tag to label nascent RNA and developed a fluorescence imaging method based on the DNA-templated oxidative amination (DTOA) reaction of s4U. The DTOA reaction occurred between amine-modified DNA and s4U-containing RNA with high sequence specificity and chemical selectivity. Target nascent mRNAs in HeLa cells, including those encoding green fluorescent proteins (GFPs) and endogenous BAG-1, were thus lit up selectively by DTOA-based fluorescence in situ hybridization (DTOA FISH). We believe the DTOA conjugation chemistry shown in this study could be generally applied to investigate the spatial distribution of nascent transcription dynamics in cellular processes.

CellRank for directed single-cell fate mapping

Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.

CellRank infers directed cell state transitions and cell fates incorporating RNA velocity information into a graph based Markov process.

Non-radioactive In Vivo Labeling of RNA with 4-Thiouracil

RNA molecules and their expression dynamics play essential roles in the establishment of complex cellular phenotypes and/or in the rapid cellular adaption to environmental changes. Accordingly, analyzing RNA expression remains an important step to understand the molecular basis controlling the formation of cellular phenotypes, cellular homeostasis or disease progression. Steady-state RNA levels in the cells are controlled by the sum of highly dynamic molecular processes contributing to RNA expression and can be classified in transcription, maturation and degradation. The main goal of analyzing RNA dynamics is to disentangle the individual contribution of these molecular processes to the life cycle of a given RNA under different physiological conditions. In the recent years, the use of nonradioactive nucleotide/nucleoside analogs and improved chemistry, in combination with time-dependent and high-throughput analysis, have greatly expanded our understanding of RNA metabolism across various cell types, organisms, and growth conditions.In this chapter, we describe a step-by-step protocol allowing pulse labeling of RNA with the nonradioactive nucleotide analog, 4-thiouracil , in the eukaryotic model organism Saccharomyces cerevisiae and the model archaeon Haloferax volcanii .