Diana Lu

Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

Recent molecular studies have shown that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels and phenotypic output, with important functional consequences. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs or proteins simultaneously, because genomic profiling methods could not be applied to single cells until very recently. Here we use single-cell RNA sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharide. We find extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit, involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.

Single cell RNA Seq reveals dynamic paracrine control of cellular variation

High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell RNA-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger RNA and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a ‘core’ module of antiviral genes is expressed very early by a few ‘precocious’ cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secretion and extracellular signalling, we show that this response is coordinated by interferon-mediated paracrine signalling from these precocious cells. Notably, preventing cell-to-cell communication also substantially reduces variability between cells in the expression of an early-induced ‘peaked’ inflammatory module, suggesting that paracrine signalling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations can use to establish complex dynamic responses.

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

Single-cell expression profiles of melanoma Tumors harbor multiple cell types that are thought to play a role in the development of resistance to drug treatments. Tirosh et al. used single-cell sequencing to investigate the distribution of these differing genetic profiles within melanomas. Many cells harbored heterogeneous genetic programs that reflected two different states of genetic expression, one of which was linked to resistance development. Following drug treatment, the resistance-linked expression state was found at a much higher level. Furthermore, the environment of the melanoma cells affected their gene expression programs. Science, this issue p. 189 Melanoma cells show transcriptional heterogeneity. To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

Whole exome sequencing of circulating tumor cells provides a window into metastatic prostate cancer

Comprehensive analyses of cancer genomes promise to inform prognoses and precise cancer treatments. A major barrier, however, is inaccessibility of metastatic tissue. A potential solution is to characterize circulating tumor cells (CTCs), but this requires overcoming the challenges of isolating rare cells and sequencing low-input material. Here we report an integrated process to isolate, qualify and sequence whole exomes of CTCs with high fidelity using a census-based sequencing strategy. Power calculations suggest that mapping of >99.995% of the standard exome is possible in CTCs. We validated our process in two patients with prostate cancer, including one for whom we sequenced CTCs, a lymph node metastasis and nine cores of the primary tumor. Fifty-one of 73 CTC mutations (70%) were present in matched tissue. Moreover, we identified 10 early trunk and 56 metastatic trunk mutations in the non-CTC tumor samples and found 90% and 73% of these mutations, respectively, in CTC exomes. This study establishes a foundation for CTC genomics in the clinic.

Dynamic profiling of the protein life cycle in response to pathogens

How the immune system readies for battle Although gene expression is tightly controlled at both the RNA and protein levels, the quantitative contribution of each step, especially during dynamic responses, remains largely unknown. Indeed, there has been much debate whether changes in RNA level contribute substantially to protein-level regulation. Jovanovic et al. built a genome-scale model of the temporal dynamics of differential protein expression during the stimulation of immunological dendritic cells (see the Perspective by Li and Biggin). Newly stimulated functions involved the up-regulation of specific RNAs and concomitant increases in the levels of the proteins they encode, whereas housekeeping functions were regulated posttranscriptionally at the protein level. Science, this issue 10.1126/science.1259038; see also p. 1066 Levels of “housekeeping” proteins are maintained directly, but those of immune response proteins depend on more transcription. [Also see Perspective by Li and Biggin] INTRODUCTION Mammalian gene expression is tightly controlled through the interplay between the RNA and protein life cycles. Although studies of individual genes have shown that regulation of each of these processes is important for correct protein expression, the quantitative contribution of each step to changes in protein expression levels remains largely unknown and much debated. Many studies have attempted to address this question in the context of steady-state protein levels, and comparing steady-state RNA and protein abundances has indicated a considerable discrepancy between RNA and protein levels. In contrast, only a few studies have attempted to shed light on how changes in each of these processes determine differential protein expression—either relative (ratios) or absolute (differences)—during dynamic responses, and only one recent report has attempted to quantitate each process. Understanding these contributions to a dynamic response on a systems scale is essential both for deciphering how cells deploy regulatory processes to accomplish physiological changes and for discovering key molecular regulators controlling each process. RATIONALE We developed an integrated experimental and computational strategy to quantitatively assess how protein levels are maintained in the context of a dynamic response and applied it to the model response of mouse immune bone marrow–derived dendritic cells (DCs) to stimulation with lipopolysaccharide (LPS). We used a modified pulsed-SILAC (stable isotope labeling with amino acids in cell culture) approach to track newly synthesized and previously labeled proteins over the first 12 hours of the response. In addition, we independently measured replicate RNA-sequencing profiles under the same conditions. We devised a computational strategy to infer per-mRNA translation rates and protein degradation rates at each time point from the temporal transcriptional profiles and pulsed-SILAC proteomics data. This allowed us to build a genome-scale quantitative model of the temporal dynamics of differential protein expression in DCs responding to LPS. RESULTS We found that before stimulation, mRNA levels contribute to overall protein expression levels more than double the combined contribution of protein translation and degradation rates. Upon LPS stimulation, changes in mRNA abundance play an even more dominant role in dynamic changes in protein levels, especially in immune response genes. Nevertheless, several protein modules—especially the preexisting proteome of proteins performing basic cellular functions—are predominantly regulated in stimulated cells at the level of protein translation or degradation, accounting for over half of the absolute change in protein molecules in the cell. In particular, despite the repression of their transcripts, the level of many proteins in the translational machinery is up-regulated upon LPS stimulation because of significantly increased translation rates, and elevated protein degradation of mitochondrial proteins plays a central role in remodeling cellular energy metabolism. CONCLUSIONS Our results support a model in which the induction of novel cellular functions is primarily driven through transcriptional changes, whereas regulation of protein production or degradation updates the levels of preexisting functions as required for an activated state. Our approach for building quantitative genome-scale models of the temporal dynamics of protein expression is broadly applicable to other dynamic systems. Dynamic protein expression regulation in dendritic cells upon stimulation with LPS. We developed an integrated experimental and computational strategy to quantitatively assess how protein levels are maintained in the context of a dynamic response. Our results support a model in which the induction of novel cellular functions is primarily driven through transcriptional changes, whereas regulation of protein production or degradation updates the levels of preexisting functions. Protein expression is regulated by the production and degradation of messenger RNAs (mRNAs) and proteins, but their specific relationships remain unknown. We combine measurements of protein production and degradation and mRNA dynamics so as to build a quantitative genomic model of the differential regulation of gene expression in lipopolysaccharide-stimulated mouse dendritic cells. Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels. Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for more than half of the absolute change in protein molecules in the cell. Thus, the proteome is regulated by transcriptional induction for newly activated cellular functions and by protein life-cycle changes for remodeling of preexisting functions.

Preparation of Single-Cell RNA-Seq Libraries for Next Generation Sequencing

For the past several decades, due to technical limitations, the field of transcriptomics has focused on population‐level measurements that can mask significant differences between individual cells. With the advent of single‐cell RNA‐Seq, it is now possible to profile the responses of individual cells at unprecedented depth and thereby uncover, transcriptome‐wide, the heterogeneity that exists within these populations. This unit describes a method that merges several important technologies to produce, in high‐throughput, single‐cell RNA‐Seq libraries. Complementary DNA (cDNA) is made from full‐length mRNA transcripts using a reverse transcriptase that has terminal transferase activity. This, when combined with a second “template‐switch” primer, allows for cDNAs to be constructed that have two universal priming sequences. Following preamplification from these common sequences, Nextera XT is used to prepare a pool of 96 uniquely indexed samples ready for Illumina sequencing. Curr. Protoc. Mol. Biol. 107:4.22.1‐4.22.17.

Abstract 993: Whole exome sequencing of CTCs as a window into metastatic cancer

Comprehensive analysis of cancer genomes in clinical settings holds the promise to inform prognoses and guide the deployment of precise cancer treatments. A major barrier, however, is the inaccessibility of adequate metastatic tissue for accurate genomic analysis. The recognition that circulating tumor cells (CTCs) are present in many advanced cancer patients suggests an exciting opportunity to overcome this challenge. For instance, if CTCs could be comprehensively sequenced, it would be possible to obtain an orthogonal sample of the tumor burden_including subsets of transiting cells bound for metastatic colonization_potentially yielding new insights to complement the static sampling of resected or biopsied lesions. We report an integrated process to isolate, qualify, and sequence whole exomes of CTCs with high fidelity, using a census-based sequencing strategy. We isolated CTCs by magnetic bead purification (Illumina MagSweeper) from the blood of patients with prostate cancer, and integrated a nanowell platform to automatically image and recover candidate single CTCs. We then developed a strategy to qualify individual CTC-derived libraries for DNA sequencing after whole genome amplification, and established an analytical framework for accurate calling of mutations using census-based sequencing and MuTect. Whole exome sequencing was performed on 20 single CTCs, obtained from a patient with advanced prostate cancer. We validated our sequencing process by comparing CTC-derived mutations to mutations found in a lymph node metastasis and nine separate cores of the primary tumor. 51 of 73 CTC mutations (70%) were observed in the metastasis or the primary tumor. Moreover, we identified 9 early trunk mutations and 56 metastatic trunk mutations in the non-CTC tumor samples and found 100% and 73% of these, respectively, in CTC exomes. Our work demonstrates the feasibility of CTC sequencing and the ability to confidently call somatic mutations. CTCs may therefore represent a non-invasive window into the mutational landscape of metastatic cancer, and may have utility for genomics in clinical practice. Citation Format: Viktor A. Adalsteinsson, Jens G. Lohr, Kristian Cibulskis, Atish D. Choudhury, Mara Rosenberg, Peter Cruz-Gordillo, Joshua Francis, ChengZhong Zhang, Alexander K. Shalek, Rahul Satija, John T. Trombetta, Diana Lu, Naren Tallapragada, Narmin T. Tahirova, Sora Kim, Brendan Blumenstiel, Carrie Sougnez, Daniel Auclair, Eliezer M. Van Allen, Mari Nakabayashi, Rosina T. Lis, Gwo-Shu M. Lee, Tiantian Li, Matthew S. Chabot, Mary-Ellen Taplin, Thomas E. Clancy, Massimo Loda, Aviv Regev, Matthew Meyerson, William C. Hahn, Philip W. Kantoff, Todd R. Golub, Gad Getz, Jesse S. Boehm, J Christopher Love. Whole exome sequencing of CTCs as a window into metastatic cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 993. doi:10.1158/1538-7445.AM2014-993

Abstract CN07-04: Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

A single tumor is composed of malignant cells in diverse genetic and epigenetic states, and this diversity presents a significant barrier for targeted therapies. Furthermore, diverse non-malignant cells, such as immune, fibroblasts and endothelial cells shape the tumor microenvironment and are emerging as important drug targets. However, the diversity of cellular states among malignant and non-malignant cells within any tumor remains poorly understood. To begin to address these challenges we applied single-cell RNA-seq to profile >3,000 single cells isolated from 16 fresh human melanomas, and characterized distinct cell types and cell states. We found that malignant cells within the same tumor display transcriptional heterogeneity associated with multiple biological processes. In particular, subpopulations of cells in treatment-naive tumors expressed a transcriptional program associated with resistance to RAF/MEK inhibition, and these were enriched in post-relapsed samples. Among several non-malignant cell types that were identified we focused on tumor-infiltrating T-cells and identified multiple profiles of exhaustion which differed among patients and could be linked to prior immunotherapies. Finally, we used our single cell–derived profiles of cell types within melanoma to deconvolve publicly available bulk tumor profiles and infer interactions between cells in the tumor microenvironment. This work demonstrates the capacity of single cell transcriptomics to offer new insights with implications for both targeted and immune therapies and will be broadly applicable to other tumor types. Citation Format: Itay Tirosh, Benjamin Izar, Sanjay M. Prakadan, Marc H. Wadsworth II, Daniel Treacy, John J. Trombetta, Diana Lu, Asaf Rotem, Christine Lian, George Murphy, Ofir Cohen, Eli van Allen, Monica Bertagnolli, Alex Genshaft, Travis K. Hughes, Carly G. K. Ziegler, Samuel W. Kazer, Aleth Gaillard, Kellie E. Kolb, Judit Valbuena1, Charles Yoon, Orit Rozenblatt-Rosen, Alex K. Shalek, Aviv Regev and Levi Garraway. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr CN07-04.