Hannah Dueck

Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue

Transcriptome profiling of single cells resident in their natural microenvironment depends upon RNA capture methods that are both noninvasive and spatially precise. We engineered a transcriptome in vivo analysis (TIVA) tag, which upon photoactivation enables mRNA capture from single cells in live tissue. Using the TIVA tag in combination with RNA sequencing (RNA-seq), we analyzed transcriptome variance among single neurons in culture and in mouse and human tissue in vivo. Our data showed that the tissue microenvironment shapes the transcriptomic landscape of individual cells. The TIVA methodology is, to our knowledge, the first noninvasive approach for capturing mRNA from live single cells in their natural microenvironment.

IVT-seq reveals extreme bias in RNA sequencing

BackgroundRNA-seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value.ResultsWe present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of over 1,000 in vitro transcribed RNAs from a full-length human cDNA library and sequenced them with polyA and total RNA-seq, the most common protocols. Because each cDNA is full length, and we show in vitro transcription is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find 50% of transcripts have more than two-fold and 10% have more than 10-fold differences in within-transcript sequence coverage. We also find greater than 6% of transcripts have regions of dramatically unpredictable sequencing coverage between samples, confounding accurate determination of their expression. We use a combination of experimental and computational approaches to show rRNA depletion is responsible for the most significant variability in coverage, and several sequence determinants also strongly influence representation.ConclusionsThese results show the utility of IVT-seq for promoting better understanding of bias introduced by RNA-seq. We find rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation. These biases suggest exon-level expression analysis may be inadvisable, and we recommend caution when interpreting RNA-seq results.

Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation

BackgroundDifferentiation of metazoan cells requires execution of different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within cells of seeming identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question.ResultsWe present high quality deep read-depth single-cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within-cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single-cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved.ConclusionsSingle-cell RNA-sequencing data provide a unique view of transcriptome function; however, careful analysis is required in order to use single-cell RNA-sequencing measurements for this purpose. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. For a subset of genes, biological variability within each cell type appears to be regulated in order to perform dynamic functions, rather than solely molecular noise.

Serotonergic neuron regulation informed by in vivo single-cell transcriptomics

Despite the recognized importance of the dorsal raphe (DR) serotonergic (5‐HT) nuclei in the pathophysiology of depression and anxiety, the molecular components/putative drug targets expressed by these neurons are poorly characterized. Utilizing the promoter of an ETS domain transcription factor that is a stable marker of 5‐HT neurons (Pet‐1) to drive 5‐HT neuronal expression of YFP, we identified 5‐HT neurons in live acute slices. We isolated RNA from single 5‐HT neurons in the ventromedial and lateral wings of the DR and performed single‐cell RNA‐Seq analysis identifying >500 G‐protein coupled receptors (GPCRs) including receptors for classical transmitters, lipid signals, and peptides as well as dozens of orphan‐GPCRs. Using these data to inform our selection of receptors to assess, we found that oxytocin and lysophosphatidic acid 1 receptors are translated and active in costimulating, with the α1‐adrenergic receptor, the firing of DR 5‐HT neurons, while the effects of histamine are inhibitory and exerted at H3 histamine receptors. The inhibitory histamine response provides evidence for tonic in vivo histamine inhibition of 5‐HT neurons. This study illustrates that unbiased single‐cell transcriptomics coupled with functional analyses provides novel insights into how neurons and neuronal systems are regulated.—Spaethling, J. M., Piel, D., Dueck, H., Buckley, P. T., Morris, J. F., Fisher, S. A., Lee, J., Sul, J.‐Y., Kim, J., Bartfai, T., Beck, S. G., Eberwine, J. H. Serotonergic neuron regulation informed by in vivo single‐cell transcriptomics. FASEB J. 28, 771–780 (2014). www.fasebj.org

Variation is function: Are single cell differences functionally important?

There is a growing appreciation of the extent of transcriptome variation across individual cells of the same cell type. While expression variation may be a byproduct of, for example, dynamic or homeostatic processes, here we consider whether single‐cell molecular variation per se might be crucial for population‐level function. Under this hypothesis, molecular variation indicates a diversity of hidden functional capacities within an ensemble of “identical” cells, and this functional diversity facilitates collective behavior that would be inaccessible to a homogenous population. In reviewing this topic, we explore possible functions that might be carried by a heterogeneous ensemble of cells; however, this question has proven difficult to test, both because methods to manipulate molecular variation are limited and because it is complicated to define, and measure, population‐level function. We consider several possible methods to further pursue the hypothesis that “variation is function” through the use of comparative analysis and novel experimental techniques.

Quantitative biology of single neurons

The building blocks of complex biological systems are single cells. Fundamental insights gained from single-cell analysis promise to provide the framework for understanding normal biological systems development as well as the limits on systems/cellular ability to respond to disease. The interplay of cells to create functional systems is not well understood. Until recently, the study of single cells has concentrated primarily on morphological and physiological characterization. With the application of new highly sensitive molecular and genomic technologies, the quantitative biochemistry of single cells is now accessible.

Assessing characteristics of RNA amplification methods for single cell RNA sequencing

BackgroundRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known.ResultsHere, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate measurements to be quantitative at an expression level greater than ~5–10 molecules.ConclusionsBased on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

SAVER: gene expression recovery for single-cell RNA sequencing

In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of genes with low or moderate expression, which hinders downstream analysis. To address this challenge, we developed SAVER (single-cell analysis via expression recovery), an expression recovery method for unique molecule index (UMI)-based scRNA-seq data that borrows information across genes and cells to provide accurate expression estimates for all genes.SAVER accurately recovers expression values in single-cell RNA-sequencing data to improve downstream analysis.

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.

Single-cell transcriptomics and functional target validation of brown adipocytes show their complex roles in metabolic homeostasis

Brown adipocytes (BAs) are specialized for adaptive thermogenesis and, upon sympathetic stimulation, activate mitochondrial uncoupling protein (UCP)‐1 and oxidize fatty acids to generate heat. The capacity for brown adipose tissue (BAT) to protect against obesity and metabolic disease is recognized, yet information about which signals activate BA, besides β3‐adrenergic receptor stimulation, is limited. Using single‐cell transcriptomics, we confirmed the presence of mRNAs encoding traditional BAT markers (i.e., UCP1, expressed in 100% of BAs Adrb3, expressed in <50% of BAs) in mouse and have shown single‐cell variability (>1000‐fold) in their expression at both the mRNA and protein levels. We further identified mRNAs encoding novel markers, orphan GPCRs, and many receptors that bind the classic neurotransmitters, neuropeptides, chemokines, cytokines, and hormones. The transcriptome variability between BAs suggests a much larger range of responsiveness of BAT than previously recognized and that not all BAs function identically. We examined the in vivo functional expression of 12 selected receptors by micro‐injecting agonists into live mouse BAT and analyzing the metabolic response. In this manner, we expanded the number of known receptors on BAs at least 25‐fold, while showing that the expression of classic BA markers is more complex and variable than previously thought.—Spaethling, J. M., Sanchez‐Alavez, M., Lee, J., Xia, F. C., Dueck, H., Wang, W., Fisher, S. A., Sul, J.‐Y., Seale, P., Kim, J., Bartfai, T., Eberwine, J. Single‐cell transcriptomics and functional target validation of brown adipocytes show their complex roles in metabolic homeostasis. FASEB J. 30, 81‐92 (2016). www.fasebj.org