Marc H. Wadsworth

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.

Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput

Single-cell RNA-seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA-seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We use Seq-Well to profile thousands of primary human macrophages exposed to Mycobacterium tuberculosis.Single-cell RNA-Seq can precisely resolve cellular states but application to sparse samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively-parallel single-cell RNA-Seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semi-permeable membrane, enabling efficient cell lysis and transcript capture. We characterize Seq-Well using species-mixing experiments and PBMCs, and profile thousands of primary human macrophages exposed to tuberculosis.

Harnessing single-cell genomics to improve the physiological fidelity of organoid-derived cell types

BackgroundSingle-cell genomic methods now provide unprecedented resolution for characterizing the component cell types and states of tissues such as the epithelial subsets of the gastrointestinal tract. Nevertheless, functional studies of these subsets at scale require faithful in vitro models of identified in vivo biology. While intestinal organoids have been invaluable in providing mechanistic insights in vitro, the extent to which organoid-derived cell types recapitulate their in vivo counterparts remains formally untested, with no systematic approach for improving model fidelity.ResultsHere, we present a generally applicable framework that utilizes massively parallel single-cell RNA-seq to compare cell types and states found in vivo to those of in vitro models such as organoids. Furthermore, we leverage identified discrepancies to improve model fidelity. Using the Paneth cell (PC), which supports the stem cell niche and produces the largest diversity of antimicrobials in the small intestine, as an exemplar, we uncover fundamental gene expression differences in lineage-defining genes between in vivo PCs and those of the current in vitro organoid model. With this information, we nominate a molecular intervention to rationally improve the physiological fidelity of our in vitro PCs. We then perform transcriptomic, cytometric, morphologic and proteomic characterization, and demonstrate functional (antimicrobial activity, niche support) improvements in PC physiology.ConclusionsOur systematic approach provides a simple workflow for identifying the limitations of in vitro models and enhancing their physiological fidelity. Using adult stem cell-derived PCs within intestinal organoids as a model system, we successfully benchmark organoid representation, relative to that in vivo, of a specialized cell type and use this comparison to generate a functionally improved in vitro PC population. We predict that the generation of rationally improved cellular models will facilitate mechanistic exploration of specific disease-associated genes in their respective cell types.

Marrying microfluidics and microwells for parallel, high-throughput single-cell genomics

An innovative, microwell-based platform for single-cell RNA sequencing (RNA-seq) combines cost efficiency, scalability and parallelizability, and will enable many new avenues of biological inquiry.See related Research article: http://dx.doi.org/10.1186/s13059-015-0684-3

Abstract PR11: Dissecting mechanisms of PD-1 blockade with single-cell RNA-sequencing

Anti-PD-1 therapy is an important new treatment option for many different types of malignancies, but overall response rates are less than 40%. We do not yet understand which patients will benefit and what resistance mechanisms allow tumor escape. The goal of this work is to understand the mechanisms by which anti-PD-1 therapy augments the anti-tumor immune response at the cellular level. Given that anti-PD-1 therapy is thought to work by altering the immunosuppressive tumor microenvironment, efforts to improve its efficacy will require a deep understanding of this complicated milieu. This will require analysis of thousands of cells using methodology that avoids the pitfalls of current techniques that have either limited scope - flow cytometry, immunohistochemistry - or limited resolution - bulk RNA sequencing. To this end, we have developed a massively parallel single-cell RNA-sequencing platform (Seq-Well) that comprehensively defines the global expression profile of all major immune lineages in the tumor microenvironment. Seq-Well uses a fabricated chip with nearly 100,000 nanowells into which barcoded beads and individual cells are distributed prior to lysis and RNA capture. Mice were implanted with two different transplantable models of cancer (MC38 colon carcinoma or B16 melanoma) and treated with anti-PD-1 or control antibodies. Tumors were harvested and CD45+ tumor-infiltrating leukocytes isolated by FACS. Thousands of cells were sequenced using Seq-Well with a median recovery of approximately 1,000 genes/cell. This level of expression diversity allows us to clearly distinguish different cell populations within the tumor microenvironment. We detect two transcriptionally distinct populations of CD8+ T cells, one that is highly proliferative (as marked by Ki-67), and one that has higher expression of perforin and TIM-3. The Ki-67+ population is enriched for a gene expression signature characterized by effector CD8+ T cells early in viral infection, consistent with their more proliferative nature. We hypothesize that this cluster of CD8+ T cells is also more functional given this signature enrichment and its lower expression of TIM-3, a marker found on exhausted CD8+ T cells. Comparisons of anti-PD-1 treated and control treated tumors are ongoing. In conclusion, massively parallel single-cell RNA-sequencing is a promising technology for the analysis of tumor immune infiltrates that will allow us to address the mechanisms by which checkpoint blockade controls tumor growth. By advancing our knowledge of an important immune checkpoint therapy, we aim to better understand who will respond to therapy, what resistance mechanisms may develop, and how to augment therapeutic efficacy with additional treatments. This abstract is also being presented as Poster A79. Citation Format: Brian C. Miller, Marc H. Wadsworth 2nd, Kevin Bi, Travis K. Hughes, Arlene H. Sharpe, Alex K. Shalek, W. Nicholas Haining. Dissecting mechanisms of PD-1 blockade with single-cell RNA-sequencing. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr PR11.

Abstract 4380: Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-sequencing

Tumors are heterogeneous ecosystems composed of genetically and epigenetically distinct cancer cell populations embedded in an intricate tumor microenvironment. The complexity and cell-to-cell interactions within this system pose a tremendous therapeutic challenge and opportunity. Due to technical constraints, current profiling technologies only provide average signals that do not reflect this intrinsic genetic and phenotypic variability. Here, we applied single-cell RNA-sequencing to examine 4,645 single cells isolated from 19 freshly procured melanomas, profiling malignant, immune and stromal cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, stem-like cells, spatial context, and a drug treatment resistance program. All tumors harbored malignant cells from two distinct transcriptional cell states, such that treatment-sensitive “MITF-high” tumors also contained drug-resistant “AXL-high” tumor cells; similar heterogeneity was present in 18 established melanoma cell lines. The frequency of AXL-high cells increased in post-relapse resistant tumors following treatment with BRAF/MEK inhibitors. Using multiplexed, quantitative single-cell immunofluorescence analysis and FACS, we validated these observations in melanoma cell lines treated with BRAF±MEK inhibitors. Signatures of cell types identified from single-cell analysis revealed distinct patterns of the tumor microenvironment. We inferred cell-to-cell interactions between stromal, immune and malignant cells, and identified factors, including known secreted gene products (e.g. CXCL12) and several complement factors. We validated the association between cancer-associated fibroblast (CAF)-expressed complement factor 3 (C3) and TIL infiltration in an independent set of 308 melanomas. Finally, analysis of TILs revealed T-cell activation dependent and independent exhaustion programs that varied among patients dependent on their exposure to treatment with immune checkpoint-inhibitors. In addition to co-expression of several known co-inhibitory receptors, including PD1, CTLA-4, and TIM-3, we identified common markers associated with cytotoxicity-independent T-cell exhaustion across patients. To identify potential T-cell clones, we classified single T-cells by their isoforms of the V and J segments of the alpha and beta TCR chains, allowing us to identify expanded T-cell clones. We found that clonally expanded T-cells expressed a strong exhaustion program, while non-expanded T-cells lacked this phenotype. This study represents the most comprehensive single-cell genomics analysis in humans to date and begins to unravel the cellular ecosystem of tumors. Single-cell genomics offer new insights with implications for both targeted and immune therapies by simultaneously profiling numerous aspects of a tumor with a single assay. Citation Format: Benjamin Izar, Itay Tirsh, Sanjay Prakadan, Marc Wadsworth, Daniel Treacy, John Trombetta, Asaf Rotem, Christine Lian, George Murphy, Mohammad Fallahi-Sichani, Ken Dutton-Regester, Jia-Ren Lin, Judit Jane-Valbuena, Orit Rozenblatt-Rosen, Charles Yoon, Alex Shalek, Aviv Regev, Levi Garraway. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4380.