Explore the vast range of titles available.

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

Single-cell molecular profiling technologies are gaining rapid traction, but the manual process by which resulting cell types are typically annotated is labor intensive and rate-limiting. We describe Garnett, a tool for rapidly annotating cell types in single-cell transcriptional profiling and single-cell chromatin accessibility datasets, based on an interpretable, hierarchical markup language of cell type-specific genes. Garnett successfully classifies cell types in tissue and whole organism datasets, as well as across species.

Dimensionality reduction is often used to visualize complex expression profiling data. Here, we use the Uniform Manifold Approximation and Projection (UMAP) method on published transcript profiles of 1484 single gene deletions of Saccharomyces cerevisiae . Proximity in low-dimensional UMAP space identifies groups of genes that correspond to protein complexes and pathways, and finds novel protein interactions, even within well-characterized complexes. This approach is more sensitive than previous methods and should be broadly useful as additional transcriptome datasets become available for other organisms. Dimensionality reduction is often used to visualize expression profiling data in order to find relationships among cells. Here, the authors use Uniform Manifold Approximation and Projection (UMAP) on published expression data of gene deletions of S. cerevisiae to find novel protein interactions.

Gene expression programs change over time, differentiation and development, and in response to stimuli. However, nearly all techniques for profiling gene expression in single cells do not directly capture transcriptional dynamics. In the present study, we present a method for combined single-cell combinatorial indexing and messenger RNA labeling (sci-fate), which uses combinatorial cell indexing and 4-thiouridine labeling of newly synthesized mRNA to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. We used sci-fate to study the cortisol response in >6,000 single cultured cells. From these data, we quantified the dynamics of the cell cycle and glucocorticoid receptor activation, and explored their intersection. Finally, we developed software to infer and analyze cell-state transitions. We anticipate that sci-fate will be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems. Gene expression dynamics in single cells are tracked by labeling newly synthesized RNA in indexed cells.

Viral infection can dramatically alter a cell’s transcriptome. However, these changes have mostly been studied by bulk measurements on many cells. Here we use single-cell mRNA sequencing to examine the transcriptional consequences of influenza virus infection. We find extremely wide cell-to-cell variation in the productivity of viral transcription – viral transcripts comprise less than a percent of total mRNA in many infected cells, but a few cells derive over half their mRNA from virus. Some infected cells fail to express at least one viral gene, but this gene absence only partially explains variation in viral transcriptional load. Despite variation in viral load, the relative abundances of viral mRNAs are fairly consistent across infected cells. Activation of innate immune pathways is rare, but some cellular genes co-vary in abundance with the amount of viral mRNA. Overall, our results highlight the complexity of viral infection at the level of single cells. When viruses infect cells, they take over the cell’s machinery and use it to express their own genes. This process has mostly been studied by looking at the average outcome of infection when many viruses infect many cells. However, it is less clear what happens in individual cells. For example, does the virus take over every cell to make lots of viral gene products, or do some cells produce far more viral gene products than others? Russell et al. have now used a new technique called single-cell RNA sequencing to look at how well influenza virus genes were expressed in hundreds of individual mammalian cells. The goal was to work out how the outcome of infection varied between different cells. One way to quantify variability – also known as heterogeneity – is by using a statistical measure called the Gini coefficient. This statistic is often used to assess the inequality in incomes across a nation.In the hypothetical situation where everyone earned the same income, the Gini coefficient would equal zero; while if only one person had all the income and all others had none, the value would be very close to one. In reality, countries fall somewhere in between these two extremes. In the United States for instance, the Gini coefficient for income is 0.47. When Russell et al. worked out the Gini coefficient for the amount of viral genes expressed in different cells, the value was at least 0.64. This indicates that there is more unevenness in viral gene expression for influenza than there is income inequality in the United States. So, what characterizes the “Bill Gates” cells and viruses that have the highest viral gene expression? Influenza viruses sometimes fail to express some of their genes. Russell et al. found that this failure often led to “poor” viruses that were less productive than “rich” viruses that expressed all the critical genes. However, the results suggest that there are also other factors that contribute a lot to the heterogeneity. Real influenza virus infections are usually started by very few viruses, so this new understanding of the variability that occurs when individual viruses infect individual cells might prove important for understanding the properties of infections at larger scales too.

Single-cell analyses have begun to provide insight into the differences among and within the individual cells that make up a tissue or organism. However, technological barriers owing to the small amount of material present in each single cell have prevented parallel analyses. Cao et al. present sci-CAR, a pooled barcode method that jointly analyzes both the RNA transcripts and chromatin profiles of single cells. By applying sci-CAR to lung adenocarcinoma cells and mouse kidney tissue, the authors demonstrate precision in assessing expression and genome accessibility at a genome-wide scale. The approach provides an improvement over bulk analysis, which can be confounded by differing cellular subgroups. Science , this issue p. 1380 A technique termed sci-CAR can assess both chromatin accessibility and RNA transcription at the single-cell level. Although we can increasingly measure transcription, chromatin, methylation, and other aspects of molecular biology at single-cell resolution, most assays survey only one aspect of cellular biology. Here we describe sci-CAR, a combinatorial indexing–based coassay that jointly profiles chromatin accessibility and mRNA (CAR) in each of thousands of single cells. As a proof of concept, we apply sci-CAR to 4825 cells, including a time series of dexamethasone treatment, as well as to 11,296 cells from the adult mouse kidney. With the resulting data, we compare the pseudotemporal dynamics of chromatin accessibility and gene expression, reconstruct the chromatin accessibility profiles of cell types defined by RNA profiles, and link cis-regulatory sites to their target genes on the basis of the covariance of chromatin accessibility and transcription across large numbers of single cells.

Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Here we investigate the transcriptional dynamics of mouse organogenesis at single-cell resolution. Using single-cell combinatorial indexing, we profiled the transcriptomes of around 2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting ‘mouse organogenesis cell atlas’ (MOCA) provides a global view of developmental processes during this critical window. We use Monocle 3 to identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. We explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle. Data from single-cell combinatorial-indexing RNA-sequencing analysis of 2 million cells from mouse embryos between embryonic days 9.5 and 13.5 are compiled in a cell atlas of mouse organogenesis, which provides a global view of developmental processes occurring during this critical period.

In recent years, macrophages have been shown to be tremendously plastic in both in vitro and in vivo settings; however, it remains unclear whether macrophages retain any persistent memory of past polarization states which may then impact their future repolarization to new states. Here, we perform deep transcriptomic profiling at high temporal resolution as macrophages are polarized with cytokines that drive them into “M1” and “M2” molecular states. We find through trajectory analysis of their global transcriptomic profiles that macrophages which are first polarized to M1 or M2 and then subsequently repolarized demonstrate little to no memory of their polarization history. We observe complete repolarization both from M1 to M2 and vice versa, and we find that macrophage transcriptional phenotypes are defined by the current cell microenvironment, rather than an amalgamation of past and present states.

High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications.

To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold “shotgun” cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type–specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.