Quantitative assessment of single-cell RNA-sequencing methods

Single-cell RNAseq for the study of isoforms-how is that possible?

Single-cell RNAseq and alternative splicing studies have recently become two of the most prominent applications of RNAseq. However, the combination of both is still challenging, and few research efforts have been dedicated to the intersection between them. Cell-level insight on isoform expression is required to fully understand the biology of alternative splicing, but it is still an open question to what extent isoform expression analysis at the single-cell level is actually feasible. Here, we establish a set of four conditions that are required for a successful single-cell-level isoform study and evaluate how these conditions are met by these technologies in published research.

Unbiased classification of mosquito blood cells by single-cell genomics and high-content imaging

Mosquito blood cells are immune cells that help control infection by vector-borne pathogens. Despite their importance, little is known about mosquito blood cell biology beyond morphological and functional criteria used for their classification. Here, we combined the power of single-cell RNA sequencing, high-content imaging flow cytometry, and single-molecule RNA hybridization to analyze a subset of blood cells of the malaria mosquito Anopheles gambiae By demonstrating that blood cells express nearly half of the mosquito transcriptome, our dataset represents an unprecedented view into their transcriptional program. Analyses of differentially expressed genes identified transcriptional signatures of two cell types and provide insights into the current classification of these cells. We further demonstrate the active transfer of a cellular marker between blood cells that may confound their identification. We propose that cell-to-cell exchange may contribute to cellular diversity and functional plasticity seen across biological systems.

powsimR: power analysis for bulk and single cell RNA-seq experiments

Summary

Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.

Availability and implementation

The R package and associated tutorial are freely available at https://github.com/bvieth/powsimR.

Contact

vieth@bio.lmu.de or hellmann@bio.lmu.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

PBMC fixation and processing for Chromium single-cell RNA sequencing

BACKGROUND

Interest in single-cell transcriptomic analysis is growing rapidly, especially for profiling rare or heterogeneous populations of cells. In almost all reported works investigators have used live cells, which introduces cell stress during preparation and hinders complex study designs. Recent studies have indicated that cells fixed by denaturing fixative can be used in single-cell sequencing, however they did not usually work with most types of primary cells including immune cells.

METHODS

The methanol-fixation and new processing method was introduced to preserve human peripheral blood mononuclear cells (PBMCs) for single-cell RNA sequencing (scRNA-Seq) analysis on 10× Chromium platform.

RESULTS

When methanol fixation protocol was broken up into three steps: fixation, storage and rehydration, we found that PBMC RNA was degraded during rehydration with PBS, not at cell fixation and up to 3-month storage steps. Resuspension but not rehydration in 3× saline sodium citrate (SSC) buffer instead of PBS preserved PBMC RNA integrity and prevented RNA leakage. Diluted SSC buffer did not interfere with full-length cDNA synthesis. The methanol-fixed PBMCs resuspended in 3× SSC were successfully implemented into 10× Chromium standard scRNA-seq workflows with no elevated low quality cells and cell doublets. The fixation process did not alter the single-cell transcriptional profiles and gene expression levels. Major subpopulations classified by marker genes could be identified in fixed PBMCs at a similar proportion as in live PBMCs. This new fixation processing protocol also worked in several other fixed primary cell types and cell lines as in live ones.

CONCLUSIONS

We expect that the methanol-based cell fixation procedure presented here will allow better and more effective batching schemes for a complex single cell experimental design with primary cells or tissues.

Single Cell RNA Sequencing of Rare Immune Cell Populations

Single-cell RNA sequencing (scRNA-Seq) is transforming our ability to characterize cells, particularly rare cells that are often overlooked in bulk population analytical approaches. This has lead to the discovery of new cell types and cellular states that echo the underlying heterogeneity and plasticity in the immune system. Technologies for the capture, sequencing, and bioinformatic analysis of single cells are rapidly improving, and scRNA-Seq is now becoming much more accessible to non-specialized laboratories. Here, we describe our experiences in adopting scRNA-Seq to the study of rare immune cells in their microanatomical niches.

Quantitative single-cell transcriptomics

Single-cell RNA sequencing (scRNA-seq) is currently transforming our understanding of biology, as it is a powerful tool to resolve cellular heterogeneity and molecular networks. Over 50 protocols have been developed in recent years and also data processing and analyzes tools are evolving fast. Here, we review the basic principles underlying the different experimental protocols and how to benchmark them. We also review and compare the essential methods to process scRNA-seq data from mapping, filtering, normalization and batch corrections to basic differential expression analysis. We hope that this helps to choose appropriate experimental and computational methods for the research question at hand.

Recent Developments in Single-Cell RNA-Seq of Microorganisms

Single-cell transcriptome analysis through next-generation sequencing (single-cell RNA-seq) has been used broadly to address important biological questions. It has proved to be very powerful, and many exciting new biological discoveries have been achieved in the last decade. Its application has greatly improved our understanding of diverse biological processes and the underlying molecular mechanisms, an understanding that would not have been achievable by conventional analysis based on bulk populations. However, so far, single-cell RNA-seq analysis has been used mostly for higher organisms. For microorganisms, single-cell RNA-seq has not been widely used, mainly because the stiff cell wall prevents effective lysis, much less starting RNA material is obtained, and the RNA lacks polyadenylated tails for universal priming of mRNA molecules. In general, the detection efficiency of current single-cell RNA-seq technologies is very low, and further development or improvement of these technologies is required for exploring the microbial world at single-cell resolution. Here, we briefly review recent developments in single-cell RNA-seq of microorganisms and discuss current challenges and future directions.

Cycling to Meet Fate: Connecting Pluripotency to the Cell Cycle

Pluripotent stem cells are characterized by their high proliferative rates, their ability to self-renew and their potential to differentiate to all the three germ layers. This rapid proliferation is brought about by a highly modified cell cycle that allows the cells to quickly shuttle from DNA synthesis to cell division, by reducing the time spent in the intervening gap phases. Many key regulators that define the somatic cell cycle are either absent or exhibit altered behavior, allowing the pluripotent cell to bypass cell cycle checkpoints typical of somatic cells. Experimental analysis of this modified stem cell cycle has been challenging due to the strong link between rapid proliferation and pluripotency, since perturbations to the cell cycle or pluripotency factors result in differentiation. Despite these hurdles, our understanding of this unique cell cycle has greatly improved over the past decade, in part because of the availability of new technologies that permit the analysis of single cells in heterogeneous populations. This review aims to highlight some of the recent discoveries in this area with a special emphasis on different states of pluripotency. We also discuss the highly interlinked network that connects pluripotency factors and key cell cycle genes and review evidence for how this interdependency may promote the rapid cell cycle. This issue gains translational importance since disruptions in stem cell proliferation and differentiation can impact disorders at opposite ends of a spectrum, from cancer to degenerative disease.

SINC-seq: correlation of transient gene expressions between nucleus and cytoplasm reflects single-cell physiology

We report a microfluidic system that physically separates nuclear RNA (nucRNA) and cytoplasmic RNA (cytRNA) from a single cell and enables single-cell integrated nucRNA and cytRNA-sequencing (SINC-seq). SINC-seq constructs two individual RNA-seq libraries, nucRNA and cytRNA, per cell, quantifies gene expression in the subcellular compartments, and combines them to create novel single-cell RNA-seq data. Leveraging SINC-seq, we discover distinct natures of correlation among cytRNA and nucRNA that reflect the transient physiological state of single cells. These data provide unique insights into the regulatory network of messenger RNA from the nucleus toward the cytoplasm at the single-cell level.

Integrating Enhancer Mechanisms to Establish a Hierarchical Blood Development Program

Hematopoietic development requires the transcription factor GATA-2, and GATA-2 mutations cause diverse pathologies, including leukemia. GATA-2-regulated enhancers increase Gata2 expression in hematopoietic stem/progenitor cells and control hematopoiesis. The +9.5-kb enhancer activates transcription in endothelium and hematopoietic stem cells (HSCs), and its deletion abrogates HSC generation. The -77-kb enhancer activates transcription in myeloid progenitors, and its deletion impairs differentiation. Since +9.5^-/- embryos are HSC deficient, it was unclear whether the +9.5 functions in progenitors or if GATA-2 expression in progenitors solely requires -77. We further dissected the mechanisms using -77;+9.5 compound heterozygous (CH) mice. The embryonic lethal CH mutation depleted megakaryocyte-erythrocyte progenitors (MEPs). While the +9.5 suffices for HSC generation, the -77 and +9.5 must reside on one allele to induce MEPs. The -77 generated burst-forming unit-erythroid through the induction of GATA-1 and other GATA-2 targets. The enhancer circuits controlled signaling pathways that orchestrate a GATA factor-dependent blood development program.

Transcriptomic Studies of Malaria: a Paradigm for Investigation of Systemic Host-Pathogen Interactions

Transcriptomics, the analysis of genome-wide RNA expression, is a common approach to investigate host and pathogen processes in infectious diseases. Technical and bioinformatic advances have permitted increasingly thorough analyses of the association of RNA expression with fundamental biology, immunity, pathogenesis, diagnosis, and prognosis. Transcriptomic approaches can now be used to realize a previously unattainable goal, the simultaneous study of RNA expression in host and pathogen, in order to better understand their interactions. This exciting prospect is not without challenges, especially as focus moves from interactions in vitro under tightly controlled conditions to tissue- and systems-level interactions in animal models and natural and experimental infections in humans. Here we review the contribution of transcriptomic studies to the understanding of malaria, a parasitic disease which has exerted a major influence on human evolution and continues to cause a huge global burden of disease. We consider malaria a paradigm for the transcriptomic assessment of systemic host-pathogen interactions in humans, because much of the direct host-pathogen interaction occurs within the blood, a readily sampled compartment of the body. We illustrate lessons learned from transcriptomic studies of malaria and how these lessons may guide studies of host-pathogen interactions in other infectious diseases. We propose that the potential of transcriptomic studies to improve the understanding of malaria as a disease remains partly untapped because of limitations in study design rather than as a consequence of technological constraints. Further advances will require the integration of transcriptomic data with analytical approaches from other scientific disciplines, including epidemiology and mathematical modeling.

Extraction of Hemocytes from Drosophila melanogaster Larvae for Microbial Infection and Analysis

During the pathogenic infection of Drosophila melanogaster, hemocytes play an important role in the immune response throughout the infection. Thus, the goal of this protocol is to develop a method to visualize the pathogen invasion in a specific immune compartment of flies, namely hemocytes. Using the method presented here, up to 3 × 10⁶ live hemocytes can be obtained from 200 Drosophila 3^rd instar larvae in 30 min for ex vivo infection. Alternatively, hemocytes can be infected in vivo through injection of 3^rd instar larvae followed by hemocyte extraction up to 24 h post-infection. These infected primary cells were fixed, stained, and imaged using confocal microscopy. Then, 3D representations were generated from the images to definitively show pathogen invasion. Additionally, high-quality RNA for qRT-PCR can be obtained for the detection of pathogen mRNA following infection, and sufficient protein can be extracted from these cells for Western blot analysis. Taken together, we present a method for definite reconciliation of pathogen invasion and confirmation of infection using bacterial and viral pathogen types and an efficient method for hemocyte extraction to obtain enough live hemocytes from Drosophila larvae for ex vivo and in vivo infection experiments.

Systematic analysis of tumour cell-extracellular matrix adhesion identifies independent prognostic factors in breast cancer

Tumour cell-extracellular matrix (ECM) interactions are fundamental for discrete steps in breast cancer progression. In particular, cancer cell adhesion to ECM proteins present in the microenvironment is critical for accelerating tumour growth and facilitating metastatic spread. To assess the utility of tumour cell-ECM adhesion as a means for discovering prognostic factors in breast cancer survival, here we perform a systematic phenotypic screen and characterise the adhesion properties of a panel of human HER2 amplified breast cancer cell lines across six ECM proteins commonly deregulated in breast cancer. We determine a gene expression signature that defines a subset of cell lines displaying impaired adhesion to laminin. Cells with impaired laminin adhesion showed an enrichment in genes associated with cell motility and molecular pathways linked to cytokine signalling and inflammation. Evaluation of this gene set in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort of 1,964 patients identifies the F12 and STC2 genes as independent prognostic factors for overall survival in breast cancer. Our study demonstrates the potential of in vitro cell adhesion screens as a novel approach for identifying prognostic factors for disease outcome.

Opportunities and Challenges in Implementation of Multiparameter Single Cell Analysis Platforms for Clinical Translation

The high-content interrogation of single cells with platforms optimized for the multiparameter characterization of cells in liquid and solid biopsy samples can enable characterization of heterogeneous populations of cells ex vivo. Doing so will advance the diagnosis, prognosis, and treatment of cancer and other diseases. However, it is important to understand the unique issues in resolving heterogeneity and variability at the single cell level before navigating the validation and regulatory requirements in order for these technologies to impact patient care. Since 2013, leading experts representing industry, academia, and government have been brought together as part of the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium to foster the potential of high-content data integration for clinical translation.

Advances in Transcriptomics: Investigating Cardiovascular Disease at Unprecedented Resolution

Whole-genome transcriptional profiling has become a standard genomic approach to investigate biological processes. RNA sequencing (RNAseq) in particular has witnessed myriad applications in genetics and various biomedical fields. RNAseq involves a relatively simple experimental protocol of RNA extraction and cDNA library preparation and, because of decreasing next-generation sequencing cost and lower computational burden for data processing, has obtained a central role in the modern biology. The recent application of RNAseq methodology to single-cell transcriptional profiling has enabled the more precise characterization of cell lineage and cell state genetic profiles. The development of bioinformatic and statistical tools has provided for differential gene expression analysis, RNA isoform analysis, haplotype-specific analysis of gene expression (allele-specific expression), and analysis of expression quantitative trait loci. We give an overview of these and recent developments in RNAseq methodology with emphasis on quality control, read mapping, feature counting, differential gene expression, allele-specific expression and expression quantitative trait loci analysis, and fusion transcript detection. We describe utilization of RNAseq as a diagnostic tool in Mendelian diseases, complex phenotypes, and cancer and give an overview of long read RNAseq technology. Furthermore, we discuss in detail the recent revolution in single-cell transcriptomics that is reshaping modern biology.

Lithium-associated transcriptional regulation of CRMP1 in patient-derived olfactory neurons and symptom changes in bipolar disorder

There is growing evidence that lithium used in the treatment of bipolar disorder (BD) affects molecular targets that are involved in neuronal growth, survival, and maturation, but it remains unclear if neuronal alterations in any of these molecules predict specific symptom changes in BD patients undergoing lithium monotherapy. The goals of this study were to (a) determine which molecular changes in the olfactory neurons of symptomatic patients receiving lithium are associated with antimanic or antidepressant response, and (b) uncover novel intraneuronal regulatory mechanisms of lithium therapy. Twenty-two treatment-naïve non-smoking patients, with symptomatic BD underwent nasal biopsies for collection of olfactory tissues, prior to their treatment and following a 6-week course of lithium monotherapy. Sixteen healthy controls were also biopsied. Combining laser capture microdissection with real-time polymerase chain reaction, we investigated baseline and treatment-associated transcriptional changes in candidate molecular targets of lithium action in the olfactory neuroepithelium. Baseline mRNA levels of glycogen synthase kinase 3 beta (GSK3β) and collapsin response mediator protein 1 (CRMP1) genes were significantly associated with BD status and with severity of mood symptoms. Among BD subjects, treatment-associated downregulation of CRMP1 expression was most predictive of decreases in both manic and depressive symptoms. This study provides a novel insight into the relevance of CRMP1, a key molecule in semaphorin-3A signaling during neurodevelopment, in the molecular mechanism of action of lithium, and in the pathophysiology of BD. It supports the use of human-derived olfactory neuronal tissues in the evaluation of treatment response of psychiatric disorders.

Enhanced Neuronal Regeneration in the CAST/Ei Mouse Strain Is Linked to Expression of Differentiation Markers after Injury

Peripheral nerve regeneration after injury requires a broad program of transcriptional changes. We investigated the basis for the enhanced nerve regenerative capacity of the CAST/Ei mouse strain relative to C57BL/6 mice. RNA sequencing of dorsal root ganglia (DRG) showed a CAST/Ei-specific upregulation of Ascl1 after injury. Ascl1 overexpression in DRG neurons of C57BL/6 mice enhanced their neurite outgrowth. Ascl1 is regulated by miR-7048-3p, which is downregulated in CAST/Ei mice. Inhibition of miR-7048-3p enhances neurite outgrowth. Following injury, CAST/Ei neurons largely retained their mature neuronal profile as determined by single-cell RNA- seq, whereas the C57BL/6 neurons acquired an immature profile. These findings suggest that one facet of the enhanced regenerative phenotype is preservation of neuronal identity in response to injury.

Genomics of coloration in natural animal populations

Animal coloration has traditionally been the target of genetic and evolutionary studies. However, until very recently, the study of the genetic basis of animal coloration has been mainly restricted to model species, whereas research on non-model species has been either neglected or mainly based on candidate approaches, and thereby limited by the knowledge obtained in model species. Recent high-throughput sequencing technologies allow us to overcome previous limitations, and open new avenues to study the genetic basis of animal coloration in a broader number of species and colour traits, and to address the general relevance of different genetic structures and their implications for the evolution of colour. In this review, we highlight aspects where genome-wide studies could be of major utility to fill in the gaps in our understanding of the biology and evolution of animal coloration. The new genomic approaches have been promptly adopted to study animal coloration although substantial work is still needed to consider a larger range of species and colour traits, such as those exhibiting continuous variation or based on reflective structures. We argue that a robust advancement in the study of animal coloration will also require large efforts to validate the functional role of the genes and variants discovered using genome-wide tools.This article is part of the themed issue 'Animal coloration: production, perception, function and application'.

Intrinsically Bayesian robust classifier for single-cell gene expression trajectories in gene regulatory networks

Background

Expression-based phenotype classification using either microarray or RNA-Seq measurements suffers from a lack of specificity because pathway timing is not revealed and expressions are averaged across groups of cells. This paper studies expression-based classification under the assumption that single-cell measurements are sampled at a sufficient rate to detect regulatory timing. Thus, observations are expression trajectories. In effect, classification is performed on data generated by an underlying gene regulatory network.

Results

Network regulation is modeled via a Boolean network with perturbation, regulation not fully determined owing to inherent biological randomness. The binary assumption is not critical because the resulting Markov chain characterizes expression trajectories. We assume a partially known Gaussian observation model belonging to an uncertainty class of models. We derive the intrinsically Bayesian robust classifier to discriminate between wild-type and mutated networks based on expression trajectories. The classifier minimizes the expected error across the uncertainty class relative to the prior distribution. We test it using a mammalian cell-cycle model, discriminating between the normal network and one in which gene p27 is mutated, thereby producing a cancerous phenotype. Tests examine all model aspects, including trajectory length, perturbation probability, and the hyperparameters governing the prior distribution over the uncertainty class.

Conclusions

Simulations show the rates at which the expected error is diminished by smaller perturbation probability, longer trajectories, and hyperparameters that tighten the prior distribution relative to the unknown true network. For average-expression measurement, methods have been proposed to obtain prior distributions. These should be extended to the more mathematically difficult, but more informative, expression trajectories.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0549-y) contains supplementary material, which is available to authorized users.

Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads

High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts, at a rate of 30-50%, and detect more genes. To demonstrate the power of Quartz-Seq2, we analyzed approximately 10,000 transcriptomes from in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of reads.

T-cell receptor-engineered T cells for cancer treatment: current status and future directions

T-cell receptor (TCR)-engineered T cells are a novel option for adoptive cell therapy used for the treatment of several advanced forms of cancer. Work using TCR-engineered T cells began more than two decades ago, with numerous preclinical studies showing that such cells could mediate tumor lysis and eradication. The success of these trials provided the foundation for clinical trials, including recent clinical successes using TCR-engineered T cells to target New York esophageal squamous cell carcinoma (NY-ESO-1). These successes demonstrate the potential of this approach to treat cancer. In this review, we provide a perspective on the current and future applications of TCR-engineered T cells for the treatment of cancer. Our summary focuses on TCR activation and both pre-clinical and clinical applications of TCR-engineered T cells. We also discuss how to enhance the function of TCR-engineered T cells and prolong their longevity in the tumor microenvironment.

MSIQ: JOINT MODELING OF MULTIPLE RNA-SEQ SAMPLES FOR ACCURATE ISOFORM QUANTIFICATION

Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call "joint modeling of multiple RNA-seq samples for accurate isoform quantification" (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ's advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.

Reconstructing differentiation networks and their regulation from time series single-cell expression data

Generating detailed and accurate organogenesis models using single-cell RNA-seq data remains a major challenge. Current methods have relied primarily on the assumption that descendant cells are similar to their parents in terms of gene expression levels. These assumptions do not always hold for in vivo studies, which often include infrequently sampled, unsynchronized, and diverse cell populations. Thus, additional information may be needed to determine the correct ordering and branching of progenitor cells and the set of transcription factors (TFs) that are active during advancing stages of organogenesis. To enable such modeling, we have developed a method that learns a probabilistic model that integrates expression similarity with regulatory information to reconstruct the dynamic developmental cell trajectories. When applied to mouse lung developmental data, the method accurately distinguished different cell types and lineages. Existing and new experimental data validated the ability of the method to identify key regulators of cell fate.

Inertial-ordering-assisted droplet microfluidics for high-throughput single-cell RNA-sequencing

Single-cell RNA-seq reveals the cellular heterogeneity inherent in the population of cells, which is very important in many clinical and research applications. Recent advances in droplet microfluidics have achieved the automatic isolation, lysis, and labeling of single cells in droplet compartments without complex instrumentation. However, barcoding errors occurring in the cell encapsulation process because of the multiple-beads-in-droplet and insufficient throughput because of the low concentration of beads for avoiding multiple-beads-in-a-droplet remain important challenges for precise and efficient expression profiling of single cells. In this study, we developed a new droplet-based microfluidic platform that significantly improved the throughput while reducing barcoding errors through deterministic encapsulation of inertially ordered beads. Highly concentrated beads containing oligonucleotide barcodes were spontaneously ordered in a spiral channel by an inertial effect, which were in turn encapsulated in droplets one-by-one, while cells were simultaneously encapsulated in the droplets. The deterministic encapsulation of beads resulted in a high fraction of single-bead-in-a-droplet and rare multiple-beads-in-a-droplet although the bead concentration increased to 1000 μl^-1, which diminished barcoding errors and enabled accurate high-throughput barcoding. We successfully validated our device with single-cell RNA-seq. In addition, we found that multiple-beads-in-a-droplet, generated using a normal Drop-Seq device with a high concentration of beads, underestimated transcript numbers and overestimated cell numbers. This accurate high-throughput platform can expand the capability and practicality of Drop-Seq in single-cell analysis.

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response

Single-cell RNA-seq is a powerful tool for revealing heterogeneity in cancer cells. However, each of the current single-cell RNA-seq platforms has inherent advantages and disadvantages. Here, we show that combining the different single-cell RNA-seq platforms can be an effective approach to obtaining complete information about expression differences and a sufficient cellular population to understand transcriptional heterogeneity in cancers. We demonstrate that it is possible to estimate missing expression information. We further demonstrate that even in the cases where precise information for an individual gene cannot be inferred, the activity of given transcriptional modules can be analyzed. Interestingly, we found that two distinct transcriptional modules, one associated with the Aurora kinase gene and the other with the DUSP gene, are aberrantly regulated in a minor population of cells and may thus contribute to the possible emergence of dormancy or eventual drug resistance within the population.

Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH

Although single-cell RNA sequencing can reliably detect large-scale transcriptional programs, it is unclear whether it accurately captures the behavior of individual genes, especially those that express only in rare cells. Here, we use single-molecule RNA fluorescence in situ hybridization as a gold standard to assess trade-offs in single-cell RNA-sequencing data for detecting rare cell expression variability. We quantified the gene expression distribution for 26 genes that range from ubiquitous to rarely expressed and found that the correspondence between estimates across platforms improved with both transcriptome coverage and increased number of cells analyzed. Further, by characterizing the trade-off between transcriptome coverage and number of cells analyzed, we show that when the number of genes required to answer a given biological question is small, then greater transcriptome coverage is more important than analyzing large numbers of cells. More generally, our report provides guidelines for selecting quality thresholds for single-cell RNA-sequencing experiments aimed at rare cell analyses.

The Listeria monocytogenes Bile Stimulon under Acidic Conditions Is Characterized by Strain-Specific Patterns and the Upregulation of Motility, Cell Wall Modification Functions, and the PrfA Regulon

Listeria monocytogenes uses a variety of transcriptional regulation strategies to adapt to the extra-host environment, the gastrointestinal tract, and the intracellular host environment. While the alternative sigma factor SigB has been proposed to be a key transcriptional regulator that facilitates L. monocytogenes adaptation to the gastrointestinal environment, the L. monocytogenes' transcriptional response to bile exposure is not well-understood. RNA-seq characterization of the bile stimulon was performed in two L. monocytogenes strains representing lineages I and II. Exposure to bile at pH 5.5 elicited a large transcriptomic response with ~16 and 23% of genes showing differential transcription in 10403S and H7858, respectively. The bile stimulon includes genes involved in motility and cell wall modification mechanisms, as well as genes in the PrfA regulon, which likely facilitate survival during the gastrointestinal stages of infection that follow bile exposure. The fact that bile exposure induced the PrfA regulon, but did not induce further upregulation of the SigB regulon (beyond that expected by exposure to pH 5.5), suggests a model where at the earlier stages of gastrointestinal infection (e.g., acid exposure in the stomach), SigB-dependent gene expression plays an important role. Subsequent exposure to bile induces the PrfA regulon, potentially priming L. monocytogenes for subsequent intracellular infection stages. Some members of the bile stimulon showed lineage- or strain-specific distribution when 27 Listeria genomes were analyzed. Even though sigB null mutants showed increased sensitivity to bile, the SigB regulon was not found to be upregulated in response to bile beyond levels expected by exposure to pH 5.5. Comparison of wildtype and corresponding ΔsigB strains newly identified 26 SigB-dependent genes, all with upstream putative SigB-dependent promoters.

A practical solution for preserving single cells for RNA sequencing

The design and implementation of single-cell experiments is often limited by their requirement for fresh starting material. We have adapted a method for histological tissue fixation using dithio-bis(succinimidyl propionate) (DSP), or Lomant’s Reagent, to stabilise cell samples for single-cell transcriptomic applications. DSP is a reversible cross-linker of free amine groups that has previously been shown to preserve tissue integrity for histology while maintaining RNA integrity and yield in bulk RNA extractions. Although RNA-seq data from DSP-fixed single cells appears to be prone to characteristic artefacts, such as slightly reduced yield of cDNA and a detectable 3′ bias in comparison with fresh cells, cell preservation using DSP does not appear to substantially reduce RNA complexity at the gene level. In addition, there is evidence that instantaneous fixation of cells can reduce inter-cell technical variability. The ability of DSP-fixed cells to retain commonly used dyes, such as propidium iodide, enables the tracking of experimental sub-populations and the recording of cell viability at the point of fixation. Preserving cells using DSP will remove several barriers in the staging of single-cell experiments, including the transport of samples and the scheduling of shared equipment for downstream single-cell isolation and processing.

Single-cell Wnt signaling niches maintain stemness of alveolar type 2 cells

Alveoli, the lung's respiratory units, are tiny sacs where oxygen enters the bloodstream. They are lined by flat alveolar type 1 (AT1) cells, which mediate gas exchange, and AT2 cells, which secrete surfactant. Rare AT2s also function as alveolar stem cells. We show that AT2 lung stem cells display active Wnt signaling, and many of them are near single, Wnt-expressing fibroblasts. Blocking Wnt secretion depletes these stem cells. Daughter cells leaving the Wnt niche transdifferentiate into AT1s: Maintaining Wnt signaling prevents transdifferentiation, whereas abrogating Wnt signaling promotes it. Injury induces AT2 autocrine Wnts, recruiting "bulk" AT2s as progenitors. Thus, individual AT2 stem cells reside in single-cell fibroblast niches providing juxtacrine Wnts that maintain them, whereas injury induces autocrine Wnts that transiently expand the progenitor pool. This simple niche maintains the gas exchange surface and is coopted in cancer.

Highly multiplexed single-cell quantitative PCR

We present a microfluidic device for rapid gene expression profiling in single cells using multiplexed quantitative polymerase chain reaction (qPCR). This device integrates all processing steps, including cell isolation and lysis, complementary DNA synthesis, pre-amplification, sample splitting, and measurement in twenty separate qPCR reactions. Each of these steps is performed in parallel on up to 200 single cells per run. Experiments performed on dilutions of purified RNA establish assay linearity over a dynamic range of at least 10⁴, a qPCR precision of 15%, and detection sensitivity down to a single cDNA molecule. We demonstrate the application of our device for rapid profiling of microRNA expression in single cells. Measurements performed on a panel of twenty miRNAs in two types of cells revealed clear cell-to-cell heterogeneity, with evidence of spontaneous differentiation manifested as distinct expression signatures. Highly multiplexed microfluidic RT-qPCR fills a gap in current capabilities for single-cell analysis, providing a rapid and cost-effective approach for profiling panels of marker genes, thereby complementing single-cell genomics methods that are best suited for global analysis and discovery. We expect this approach to enable new studies requiring fast, cost-effective, and precise measurements across hundreds of single cells.

Concentrating and labeling genomic DNA in a nanofluidic array

Nucleotide incorporation by DNA polymerase forms the basis of DNA sequencing-by-synthesis. In current platforms, either the single-stranded DNA or the enzyme is immobilized on a solid surface to locate the incorporation of individual nucleotides in space and/or time. Solid-phase reactions may, however, hinder the polymerase activity. We demonstrate a device and a protocol for the enzymatic labeling of genomic DNA arranged in a dense array of single molecules without attaching the enzyme or the DNA to a surface. DNA molecules accumulate in a dense array of pits embedded within a nanoslit due to entropic trapping. We then perform ϕ29 polymerase extension from single-strand nicks created on the trapped molecules to incorporate fluorescent nucleotides into the DNA. The array of entropic traps can be loaded with λ-DNA molecules to more than 90% of capacity at a flow rate of 10 pL min^-1. The final concentration can reach up to 100 μg mL^-1, and the DNA is eluted from the array by increasing the flow rate. The device may be an important preparative module for carrying out enzymatic processing on DNA extracted from single-cells in a microfluidic chip.

Automated cell-type classification in intact tissues by single-cell molecular profiling

A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) with exceptional signal strength, specificity, and sensitivity in tissue. Multiplexed data sets can be acquired using barcoded probes and rapid label-image-erase cycles, with automated calculation of single cell profiles, enabling clustering and anatomical re-mapping of cells. We apply PLISH to expression profile ~2900 cells in intact mouse lung, which identifies and localizes known cell types, including rare ones. Unsupervised classification of the cells indicates differential expression of ‘housekeeping’ genes between cell types, and re-mapping of two sub-classes of Club cells highlights their segregated spatial domains in terminal airways. By enabling single cell profiling of various RNA species in situ, PLISH can impact many areas of basic and medical research.

The human body contains several hundred types of specialized cells that have different roles. The cells form tissues, and each tissue can only work if it has the right cells and if they are correctly organized and distributed to build working structures. This is how the same body parts, e.g. lungs, brains, hearts, end up looking and working the same way in almost everyone.

Cells organize themselves into tissues by exchanging short-range messages between nearby cells. Understanding how cells communicate to form, maintain and repair tissues is a challenge for biologists. Finding ways to examine different signals at the same time would improve our understanding of these important processes.

Now, Nagendran, Riordan et al. developed a microscopy technique that can tackle this issue. The cells can be stained and tagged with already established dyes and markers to measure the location and signals of all cell groups at the same time. By calculating how these cells are distributed in space it is then possible to estimate how they interact with each other. Based on this, Nagendran, Riordan et al. then successfully tested their tool in lung tissues from mice and humans.

This cheap, high-speed technology works on tissue samples from any animal, including humans, and can be easily combined with existing technologies and so be adapted for a wide range of uses. A deeper knowledge of how combinations of signals guide tissue formation and maintenance could help us to better understand what causes developmental diseases, organ failures and cancer. Tools like this could even help to identify key targets for new treatments.

netSmooth: Network-smoothing based imputation for single cell RNA-seq

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

netSmooth: Network-smoothing based imputation for single cell RNA-seq

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

netSmooth: Network-smoothing based imputation for single cell RNA-seq

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

Cell-Specific RNA Quantification in Human SN DA Neurons from Heterogeneous Post-mortem Midbrain Samples by UV-Laser Microdissection and RT-qPCR

Cell specificity of gene expression analysis is from particular relevance when the abundance of target cells is not homogeneous in the compared tissue samples, like it is the case, e.g., when comparing brain tissues from controls and in neurodegenerative disease states. While single-cell gene expression profiling is already a methodological challenge per se, it becomes even more prone to artifacts when analyzing individual cells from human post-mortem samples. Not only because human samples can never be matched as precisely as those from animal models, but also, because the RNA-quality that can be obtained from human samples usually displays a high range of variability. Here, we detail our most actual method for combining contact-free UV-laser microdissection (UV-LMD) with reverse transcription and quantitative PCR (RT-qPCR) that addresses all these issues. We specifically optimized our protocols to quantify and compare mRNA as well as miRNA levels in human neurons from post-mortem brain tissue. As human post-mortem tissue samples are never perfectly matched (e.g., in respect to distinct donor ages and RNA integrity numbers RIN), we refined data analysis by applying a linear mixed effects model to RT-qPCR data, which allows dissecting and subtracting linear contributions of distinct confounders on detected gene expression levels (i.e., RIN, age). All these issues were considered for comparative gene expression analysis in dopamine (DA) midbrain neurons of the Substantia nigra (SN) from controls and Parkinson's disease (PD) specimens, as the preferential degeneration of SN DA neurons in the pathological hallmark of PD. By utilizing the here-described protocol we identified that a variety of genes-encoding for ion channels, dopamine metabolism proteins, and PARK gene products-display a transcriptional dysregulation in remaining human SN DA neurons from PD brains compared to those of controls. We show that the linear mixed effects model allows further stratification of RT-qPCR data, as it indicated that differential gene expression of some genes was rather correlated with different ages of the analyzed human brain samples than with the disease state.

Bringing Renal Biopsy Interpretation Into the Molecular Age With Single-Cell RNA Sequencing

The renal biopsy provides critical diagnostic and prognostic information to clinicians including cases of acute kidney injury, chronic kidney disease, and allograft dysfunction. Today, biopsy specimens are read using a combination of light microscopy, electron microscopy, and indirect immunofluorescence, with a limited number of antibodies. These techniques all were perfected decades ago with only incremental changes since then. By contrast, recent advances in single-cell genomics are transforming scientists' ability to characterize cells. Rather than measure the expression of several genes at a time by immunofluorescence, it now is possible to measure the expression of thousands of genes in thousands of single cells simultaneously. Here, we argue that the development of single-cell RNA sequencing offers an opportunity to describe human kidney disease comprehensively at a cellular level. It is particularly well suited for the analysis of immune cells, which are characterized by multiple subtypes and changing functions depending on their environment. In this review, we summarize the development of single-cell RNA sequencing methodologies. We discuss how these approaches are being applied in other organs, and the potential for this powerful technology to transform our understanding of kidney disease once applied to the renal biopsy.

Classification of State Trajectories in Gene Regulatory Networks

Gene-expression-based phenotype classification is used for disease diagnosis and prognosis relating to treatment strategies. The present paper considers classification based on sequential measurements of multiple genes using gene regulatory network (GRN) modeling. There are two networks, original and mutated, and observations consist of trajectories of network states. The problem is to classify an observation trajectory as coming from either the original or mutated network. GRNs are modeled via probabilistic Boolean networks, which incorporate stochasticity at both the gene and network levels. Mutation affects the regulatory logic. Classification is based upon observing a trajectory of states of some given length. We characterize the Bayes classifier and find the Bayes error for a general PBN and the special case of a single Boolean network affected by random perturbations (BNp). The Bayes error is related to network sensitivity, meaning the extent of alteration in the steady-state distribution of the original network owing to mutation. Using standard methods to calculate steady-state distributions is cumbersome and sometimes impossible, so we provide an efficient algorithm and approximations. Extensive simulations are performed to study the effects of various factors, including approximation accuracy. We apply the classification procedure to a p53 BNp and a mammalian cell cycle PBN.

High Throughput Single Cell RNA Sequencing, Bioinformatics Analysis and Applications

Single cell sequencing (SCS) can be harnessed to acquire the genomes, transcriptomes and epigenomes from individual cells. Next generation sequencing (NGS) technology is the driving force for single cell sequencing. scRNA-seq requires a lengthy pipeline comprising of single cell sorting, RNA extraction, reverse transcription, amplification, library construction, sequencing and subsequent bioinformatic analysis. Computational algorithms are essential to fulfill many tasks of interest using scRNA-seq data. scRNA-seq has already enabled researchers to revisit long-standing questions in cancer biology, including cancer metastasis, heterogeneity and evolution. Circulating Tumor Cells (CTC) are not only an important mechanism for cancer metastasis, but also provide a possibility to diagnose and monitor cancer in a convenient way independent of surgical resection of the cancer.

Cancer transcriptome profiling at the juncture of clinical translation

Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.

Injury Activates Transient Olfactory Stem Cell States with Diverse Lineage Capacities

Tissue homeostasis and regeneration are mediated by programs of adult stem cell renewal and differentiation. However, the mechanisms that regulate stem cell fates under such widely varying conditions are not fully understood. Using single-cell techniques, we assessed the transcriptional changes associated with stem cell self-renewal and differentiation and followed the maturation of stem cell-derived clones using sparse lineage tracing in the regenerating mouse olfactory epithelium. Following injury, quiescent olfactory stem cells rapidly shift to activated, transient states unique to regeneration and tailored to meet the demands of injury-induced repair, including barrier formation and proliferation. Multiple cell fates, including renewed stem cells and committed differentiating progenitors, are specified during this early window of activation. We further show that Sox2 is essential for cells to transition from the activated to neuronal progenitor states. Our study highlights strategies for stem cell-mediated regeneration that may be conserved in other adult stem cell niches.

SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells

Simultaneous sequencing of the genome and transcriptome at the single-cell level is a powerful tool for characterizing genomic and transcriptomic variation and revealing correlative relationships. However, it remains technically challenging to analyze both the genome and transcriptome in the same cell. Here, we report a novel method for simultaneous isolation of genomic DNA and total RNA (SIDR) from single cells, achieving high recovery rates with minimal cross-contamination, as is crucial for accurate description and integration of the single-cell genome and transcriptome. For reliable and efficient separation of genomic DNA and total RNA from single cells, the method uses hypotonic lysis to preserve nuclear lamina integrity and subsequently captures the cell lysate using antibody-conjugated magnetic microbeads. Evaluating the performance of this method using real-time PCR demonstrated that it efficiently recovered genomic DNA and total RNA. Thorough data quality assessments showed that DNA and RNA simultaneously fractionated by the SIDR method were suitable for genome and transcriptome sequencing analysis at the single-cell level. The integration of single-cell genome and transcriptome sequencing by SIDR (SIDR-seq) showed that genetic alterations, such as copy-number and single-nucleotide variations, were more accurately captured by single-cell SIDR-seq compared with conventional single-cell RNA-seq, although copy-number variations positively correlated with the corresponding gene expression levels. These results suggest that SIDR-seq is potentially a powerful tool to reveal genetic heterogeneity and phenotypic information inferred from gene expression patterns at the single-cell level.

Network embedding-based representation learning for single cell RNA-seq data

Single cell RNA-seq (scRNA-seq) techniques can reveal valuable insights of cell-to-cell heterogeneities. Projection of high-dimensional data into a low-dimensional subspace is a powerful strategy in general for mining such big data. However, scRNA-seq suffers from higher noise and lower coverage than traditional bulk RNA-seq, hence bringing in new computational difficulties. One major challenge is how to deal with the frequent drop-out events. The events, usually caused by the stochastic burst effect in gene transcription and the technical failure of RNA transcript capture, often render traditional dimension reduction methods work inefficiently. To overcome this problem, we have developed a novel Single Cell Representation Learning (SCRL) method based on network embedding. This method can efficiently implement data-driven non-linear projection and incorporate prior biological knowledge (such as pathway information) to learn more meaningful low-dimensional representations for both cells and genes. Benchmark results show that SCRL outperforms other dimensional reduction methods on several recent scRNA-seq datasets.

The promise of single-cell RNA sequencing for kidney disease investigation

Recent techniques for single-cell RNA sequencing (scRNA-seq) at high throughput are leading to profound new discoveries in biology. The ability to generate vast amounts of transcriptomic data at cellular resolution represents a transformative advance, allowing the identification of novel cell types, states, and dynamics. In this review, we summarize the development of scRNA-seq methodologies and highlight their advantages and drawbacks. We discuss available software tools for analyzing scRNA-Seq data and summarize current computational challenges. Finally, we outline ways in which this powerful technology might be applied to discovery research in kidney development and disease.

High-throughput full-length single-cell mRNA-seq of rare cells

Single-cell characterization techniques, such as mRNA-seq, have been applied to a diverse range of applications in cancer biology, yielding great insight into mechanisms leading to therapy resistance and tumor clonality. While single-cell techniques can yield a wealth of information, a common bottleneck is the lack of throughput, with many current processing methods being limited to the analysis of small volumes of single cell suspensions with cell densities on the order of 10⁷ per mL. In this work, we present a high-throughput full-length mRNA-seq protocol incorporating a magnetic sifter and magnetic nanoparticle-antibody conjugates for rare cell enrichment, and Smart-seq2 chemistry for sequencing. We evaluate the efficiency and quality of this protocol with a simulated circulating tumor cell system, whereby non-small-cell lung cancer cell lines (NCI-H1650 and NCI-H1975) are spiked into whole blood, before being enriched for single-cell mRNA-seq by EpCAM-functionalized magnetic nanoparticles and the magnetic sifter. We obtain high efficiency (> 90%) capture and release of these simulated rare cells via the magnetic sifter, with reproducible transcriptome data. In addition, while mRNA-seq data is typically only used for gene expression analysis of transcriptomic data, we demonstrate the use of full-length mRNA-seq chemistries like Smart-seq2 to facilitate variant analysis of expressed genes. This enables the use of mRNA-seq data for differentiating cells in a heterogeneous population by both their phenotypic and variant profile. In a simulated heterogeneous mixture of circulating tumor cells in whole blood, we utilize this high-throughput protocol to differentiate these heterogeneous cells by both their phenotype (lung cancer versus white blood cells), and mutational profile (H1650 versus H1975 cells), in a single sequencing run. This high-throughput method can help facilitate single-cell analysis of rare cell populations, such as circulating tumor or endothelial cells, with demonstrably high-quality transcriptomic data.

Single-cell transcriptomic analysis of oligodendrocyte lineage cells

Oligodendrocytes (OLs) are glial cells in the central nervous system (CNS), which produce myelin, a lipid-rich membrane that insulates neuronal axons. The main function ascribed to OLs is to regulate the speed of electric pulse transmission, and as such OLs have been widely considered as a single and discrete population. Nevertheless, OLs and their precursor cells (OPCs) throughout the CNS have different morphologies and regional functional differences have been observed. Moreover, OLs have recently been involved in other functional processes such as metabolic coupling with axons. In this review, we focus on recent advances in single-cell transcriptomics suggesting that OLs are more heterogeneous than previously thought, with defined subpopulations and cell states that are associated with different stages of lineage progression and might also represent distinct functional states.

Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte

Direct lineage conversion offers a new strategy for tissue regeneration and disease modelling. Despite recent success in directly reprogramming fibroblasts into various cell types, the precise changes that occur as fibroblasts progressively convert to the target cell fates remain unclear. The inherent heterogeneity and asynchronous nature of the reprogramming process renders it difficult to study this process using bulk genomic techniques. Here we used single-cell RNA sequencing to overcome this limitation and analysed global transcriptome changes at early stages during the reprogramming of mouse fibroblasts into induced cardiomyocytes (iCMs). Using unsupervised dimensionality reduction and clustering algorithms, we identified molecularly distinct subpopulations of cells during reprogramming. We also constructed routes of iCM formation, and delineated the relationship between cell proliferation and iCM induction. Further analysis of global gene expression changes during reprogramming revealed unexpected downregulation of factors involved in mRNA processing and splicing. Detailed functional analysis of the top candidate splicing factor, Ptbp1, revealed that it is a critical barrier for the acquisition of cardiomyocyte-specific splicing patterns in fibroblasts. Concomitantly, Ptbp1 depletion promoted cardiac transcriptome acquisition and increased iCM reprogramming efficiency. Additional quantitative analysis of our dataset revealed a strong correlation between the expression of each reprogramming factor and the progress of individual cells through the reprogramming process, and led to the discovery of new surface markers for the enrichment of iCMs. In summary, our single-cell transcriptomics approaches enabled us to reconstruct the reprogramming trajectory and to uncover intermediate cell populations, gene pathways and regulators involved in iCM induction.