Cole Trapnell

Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing

Chromatin state and the single cell Identifying the chromatin state of any single cell, which may or may not have a different function or represent different stages relative to others collected within any single culture, experiment, or tissue, has been challenging. Cusanovitch et al. skirted previously identified technological limitations to identify regions of accessible chromatin at single-cell resolution. Combinatorial cellular indexing, a strategy for multiplex barcoding of thousands of single cells per experiment, was successfully used to investigate the genome-wide chromatin accessibility landscape in each of over 15,000 single cells. Science, this issue p. 910 Combinatorial indexing can identify chromatin states at single-cell resolution. Technical advances have enabled the collection of genome and transcriptome data sets with single-cell resolution. However, single-cell characterization of the epigenome has remained challenging. Furthermore, because cells must be physically separated before biochemical processing, conventional single-cell preparatory methods scale linearly. We applied combinatorial cellular indexing to measure chromatin accessibility in thousands of single cells per assay, circumventing the need for compartmentalization of individual cells. We report chromatin accessibility profiles from more than 15,000 single cells and use these data to cluster cells on the basis of chromatin accessibility landscapes. We identify modules of coordinately regulated chromatin accessibility at the level of single cells both between and within cell types, with a scalable method that may accelerate progress toward a human cell atlas.

Pulmonary macrophage transplantation therapy

Bone-marrow transplantation is an effective cell therapy but requires myeloablation, which increases infection risk and mortality. Recent lineage-tracing studies documenting that resident macrophage populations self-maintain independently of haematological progenitors prompted us to consider organ-targeted, cell-specific therapy. Here, using granulocyte–macrophage colony-stimulating factor (GM-CSF) receptor-β-deficient (Csf2rb−/−) mice that develop a myeloid cell disorder identical to hereditary pulmonary alveolar proteinosis (hPAP) in children with CSF2RA or CSF2RB mutations, we show that pulmonary macrophage transplantation (PMT) of either wild-type or Csf2rb-gene-corrected macrophages without myeloablation was safe and well-tolerated and that one administration corrected the lung disease, secondary systemic manifestations and normalized disease-related biomarkers, and prevented disease-specific mortality. PMT-derived alveolar macrophages persisted for at least one year as did therapeutic effects. Our findings identify mechanisms regulating alveolar macrophage population size in health and disease, indicate that GM-CSF is required for phenotypic determination of alveolar macrophages, and support translation of PMT as the first specific therapy for children with hPAP.

Comprehensive single-cell transcriptional profiling of a multicellular organism

Sequencing each cell of the nematode Single-cell sequencing is challenging owing to the limited biological material available in an individual cell and the high cost of sequencing across multiple cells. Cao et al. developed a two-step combinatorial barcoding method to profile both single-cell and single-nucleus transcriptomes without requiring physical isolation of each cell. The authors profiled almost 50,000 single cells from an individual Caenorhabditis elegans larval stage and were able to identify and recover information from different, even rare, cell types. Science, this issue p. 661 Single-cell combinatorial indexing RNA sequencing achieves more than 50-fold cellular coverage of a developing nematode worm. 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.

Single-cell transcriptomics reveals receptor transformations during olfactory neurogenesis

Maturation of olfactory neurons The sense of smell depends on neurons in the olfactory epithelium to perceive chemical scents. Each neuron specializes with one receptor. Hanchate et al. now show that the one-for-one relationship is not as simple as thought. As new neurons develop to replenish the olfactory epithelium, they initially express several different alleles of olfactory receptors. Then, as each neuron matures, they specialize to express a single receptor. Science, this issue p. 1251 Olfactory neurons progress through development from expressing several to favoring one of the many thousands of olfactory receptors available. The sense of smell allows chemicals to be perceived as diverse scents. We used single-neuron RNA sequencing to explore the developmental mechanisms that shape this ability as nasal olfactory neurons mature in mice. Most mature neurons expressed only one of the ~1000 odorant receptor genes (Olfrs) available, and at a high level. However, many immature neurons expressed low levels of multiple Olfrs. Coexpressed Olfrs localized to overlapping zones of the nasal epithelium, suggesting regional biases, but not to single genomic loci. A single immature neuron could express Olfrs from up to seven different chromosomes. The mature state in which expression of Olfr genes is restricted to one per neuron emerges over a developmental progression that appears to be independent of neuronal activity involving sensory transduction molecules.

Integrative Analyses of Human Reprogramming Reveal Dynamic Nature of Induced Pluripotency

Induced pluripotency is a promising avenue for disease modeling and therapy, but the molecular principles underlying this process, particularly in human cells, remain poorly understood due to donor-to-donor variability and intercellular heterogeneity. Here, we constructed and characterized a clonal, inducible human reprogramming system that provides a reliable source of cells at any stage of the process. This system enabled integrative transcriptional and epigenomic analysis across the human reprogramming timeline at high resolution. We observed distinct waves of gene network activation, including the ordered re-activation of broad developmental regulators followed by early embryonic patterning genes and culminating in the emergence of a signature reminiscent of pre-implantation stages. Moreover, complementary functional analyses allowed us to identify and validate novel regulators of the reprogramming process. Altogether, this study sheds light on the molecular underpinnings of induced pluripotency in human cells and provides a robust cell platform for further studies. PAPERCLIP.

Single-cell transcriptome sequencing: recent advances and remaining challenges

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.

The cis -regulatory dynamics of embryonic development at single-cell resolution

Understanding how gene regulatory networks control the progressive restriction of cell fates is a long-standing challenge. Recent advances in measuring gene expression in single cells are providing new insights into lineage commitment. However, the regulatory events underlying these changes remain unclear. Here we investigate the dynamics of chromatin regulatory landscapes during embryogenesis at single-cell resolution. Using single-cell combinatorial indexing assay for transposase accessible chromatin with sequencing (sci-ATAC-seq), we profiled chromatin accessibility in over 20,000 single nuclei from fixed Drosophila melanogaster embryos spanning three landmark embryonic stages: 2–4 h after egg laying (predominantly stage 5 blastoderm nuclei), when each embryo comprises around 6,000 multipotent cells; 6–8 h after egg laying (predominantly stage 10–11), to capture a midpoint in embryonic development when major lineages in the mesoderm and ectoderm are specified; and 10–12 h after egg laying (predominantly stage 13), when each of the embryo’s more than 20,000 cells are undergoing terminal differentiation. Our results show that there is spatial heterogeneity in the accessibility of the regulatory genome before gastrulation, a feature that aligns with future cell fate, and that nuclei can be temporally ordered along developmental trajectories. During mid-embryogenesis, tissue granularity emerges such that individual cell types can be inferred by their chromatin accessibility while maintaining a signature of their germ layer of origin. Analysis of the data reveals overlapping usage of regulatory elements between cells of the endoderm and non-myogenic mesoderm, suggesting a common developmental program that is reminiscent of the mesendoderm lineage in other species. We identify 30,075 distal regulatory elements that exhibit tissue-specific accessibility. We validated the germ-layer specificity of a subset of these predicted enhancers in transgenic embryos, achieving an accuracy of 90%. Overall, our results demonstrate the power of shotgun single-cell profiling of embryos to resolve dynamic changes in the chromatin landscape during development, and to uncover the cis-regulatory programs of metazoan germ layers and cell types.

Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

Conventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold “shotgun cellular coverage” of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

Reversed graph embedding resolves complex single-cell developmental trajectories

Organizing single cells along a developmental trajectory has emerged as a powerful tool for understanding how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood development, and show that loss of function mutations in key lineage transcription factors diverts cells to alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with single-cell genomics.

Extreme heterogeneity of influenza virus infection in single 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.