Nalin Leelatian

Single cell analysis of human tissues and solid tumors with mass cytometry

Mass cytometry measures 36 or more markers per cell and is an appealing platform for comprehensive phenotyping of cells in human tissue and tumor biopsies. While tissue disaggregation and fluorescence cytometry protocols were pioneered decades ago, it is not known whether established protocols will be effective for mass cytometry and maintain cancer and stromal cell diversity.

Characterizing Phenotypes and Signaling Networks of Single Human Cells by Mass Cytometry

Single cell mass cytometry is revolutionizing our ability to quantitatively characterize cellular biomarkers and signaling networks. Mass cytometry experiments routinely measure 25-35 features of each cell in primary human tissue samples. The relative ease with which a novice user can generate a large amount of high quality data and the novelty of the approach have created a need for example protocols, analysis strategies, and datasets. In this chapter, we present detailed protocols for two mass cytometry experiments designed as training tools. The first protocol describes detection of 26 features on the surface of human peripheral blood mononuclear cells. In the second protocol, a mass cytometry signaling network profile measures 25 node states comprised of five key signaling effectors (AKT, ERK1/2, STAT1, STAT5, and p38) quantified under five conditions (Basal, FLT3L, SCF, IL-3, and IFNγ). This chapter compares manual and unsupervised data analysis approaches, including bivariate plots, heatmaps, histogram overlays, SPADE, and viSNE. Data files in this chapter have been shared online using Cytobank ( http://www.cytobank.org/irishlab/ ).

Characterizing cell subsets using marker enrichment modeling

Learning cell identity from high-content single-cell data presently relies on human experts. We present marker enrichment modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human- and machine-readable text label. MEM outperforms traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.

Discovery of human cell selective effector molecules using single cell multiplexed activity metabolomics

Discovering bioactive metabolites within a metabolome is challenging because there is generally little foreknowledge of metabolite molecular and cell-targeting activities. Here, single-cell response profiles and primary human tissue comprise a response platform used to discover novel microbial metabolites with cell-type-selective effector properties in untargeted metabolomic inventories. Metabolites display diverse effector mechanisms, including targeting protein synthesis, cell cycle status, DNA damage repair, necrosis, apoptosis, or phosphoprotein signaling. Arrayed metabolites are tested against acute myeloid leukemia patient bone marrow and molecules that specifically targeted blast cells or nonleukemic immune cell subsets within the same tissue biopsy are revealed. Cell-targeting polyketides are identified in extracts from biosynthetically prolific bacteria, including a previously unreported leukemia blast-targeting anthracycline and a polyene macrolactam that alternates between targeting blasts or nonmalignant cells by way of light-triggered photochemical isomerization. High-resolution cell profiling with mass cytometry confirms response mechanisms and is used to validate initial observations.Bioactive metabolites from plant and microbial extracts hold therapeutic potential. Here, the authors combine untargeted metabolomic arrays with flow cytometry-based single cell response profiling and identify metabolites with cell subset-specific activities in the bone marrow from an AML patient.

Preparing Viable Single Cells from Human Tissue and Tumors for Cytomic Analysis

Mass cytometry is a single‐cell biology technique that samples >500 cells per second, measures >35 features per cell, and is sensitive across a dynamic range of >104 relative intensity units per feature. This combination of technical assets has powered a series of recent cytomic studies where investigators used mass cytometry to measure protein and phospho‐protein expression in millions of cells, characterize rare cell types in healthy and diseased tissues, and reveal novel, unexpected cells. However, these advances largely occurred in studies of blood, lymphoid tissues, and bone marrow, since the cells in these tissues are readily obtained in single‐cell suspensions. This unit establishes a primer for single‐cell analysis of solid tumors and tissues, and has been tested with mass cytometry. The cells obtained from these protocols can be fixed for study, cryopreserved for long‐term storage, or perturbed ex vivo to dissect responses to stimuli and inhibitors.

Abstract 3935: Dissecting small cell lung carcinoma heterogeneity and chemotherapy resistance with mass cytometry

Introduction: Small cell lung cancer (SCLC) is a high grade neuroendocrine carcinoma of the lung responsible for up to 25% of lung cancer deaths. Treatment in SCLC has not changed significantly in the last 20 years. SCLC initially responds well to chemotherapy, but inevitably recurs. Characterization of tumor heterogeneity and changes in SCLC cell signaling and phenotypes after chemotherapy could yield new insights and therapeutic options. Mass cytometry uses metal labeled antibodies to profile expression and phosphorylation of more than 40 proteins in single cells and offers the opportunity to identify new subpopulations including potential cancer stem cell populations as well as targets for novel therapies in SCLC. Methods: Nude mice with SCLC patient derived xenografts (PDXs) were treated with one cycle of carboplatin/etoposide or saline injection. Tumors were harvested at ~2000mm3, disaggregated, and cryopreserved. PDX samples were stained with a 22 marker panel and an intercalator dye to identify nucleated cells. This panel measured phospho-signaling, neuroendocrine, immune, and mesenchymal cell markers, and functional markers including ki67 and cleaved caspase 3. ViSNE analysis and biaxial gating were used to identify major subpopulations of interest. Results: PDX tumors released viable tumor and stromal cells suitable for cryopreservation and mass cytometry. ACK buffer and enzymatic dissociation yielded the best quality cells by depleting red blood cells. Mouse cells, including leukocytes, were excluded using mouse MHC1 gating and iridium intercalator was used to identify nucleated cells. Single cell protein expression and phosphorylation was analyzed using viSNE and yielded at least 9 distinct subpopulations based on density islands with neuroendocrine (CD56+) and non-neuroendocrine (CD56-) populations. Chemotherapy treated cells had dramatic changes in subpopulation distribution compared to matched mock treated tumor. This included 2-3 fold expansion of SOX2+, CD117+, and pSTAT3+ populations with chemotherapy treatment. A small CD44+ tumor subpopulation identified in the chemotherapy treated cells was not present in the matched mock treated tumor suggesting a potential chemotherapy resistant/ stem- like subpopulation. Kinase activity showed stable p-AKT overall, but increased p-S6 in the chemotherapy treated cells. Conclusions: Mass cytometry was able to identify multiple neuroendocrine and non-neuroendocrine cell populations from SCLC PDXs and characterize their signaling. Chemotherapy treated PDX had differential subpopulation distribution with enrichment of multiple stem-like signaling factors. This work demonstrates the utility of mass cytometry and viSNE as novel techniques to identify subpopulations associated with chemotherapy resistance for future targeting and demonstrates the feasibility of this technique for characterizing signaling heterogeneity in human SCLC tumors. Citation Format: Jonathan M. Lehman, Nalin Leelatian, Bradford Harris, Megan Hoeksema, Zou Yong, Deon B. Doxie, Jonathan M. Irish, Pierre P. Massion. Dissecting small cell lung carcinoma heterogeneity and chemotherapy resistance with mass cytometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3935. doi:10.1158/1538-7445.AM2017-3935

Abstract 364: Mass cytometry of human glioblastoma characterizes more than 99 percent of cells and reveals intratumoral cell subsets defined by contrasting signaling network profiles

Background: Glioblastoma (GBM) remains largely incurable despite intense study of resected tissue. Prior studies have revealed GBM cell subsets (Patel et al., Science 2014) and have implicated subset emergence as a potential mechanism of poor outcome in other cancer types. Signaling in rare cells or a mix of cell subsets may enable therapy resistance and recurrence of GBM. For example, STAT3 RNA expression has been previously shown to correlate with poor outcome in GBM (Jahani-Asl et al., Nat Neurosci 2016 and TCGA). The complexity of GBM, combined with the interconnectedness between cancer and host cells in the microenvironment, means that a single cell biology approach is needed to comprehensively characterize patient biopsy cells and determine how protein expression, signaling, and functional capabilities impact treatment response. Methods: We developed a novel mass cytometry approach to characterize human GBM that identified ~90-95% of tumor cells (Leelatian & Doxie et al., Cytometry B 2016). Here, we applied this approach using a newly created 35-antibody mass cytometry panel focused on basal phospho-protein signaling. The published panel of 16 identity proteins included SOX2, CD44, Nestin, PDGFRα, S100B, and NCAM. This panel was augmented to measure 10 additional proteins and 9 phospho-proteins including p-STAT3, p-EGFR, and p-NFκB. Signaling measurements were chosen to match prior single cell studies of signaling networks that stratified clinical outcomes in blood cancers (Irish et al., Cell 2004; PNAS 2010, Levine et al., Cell 2015). Between 10,000 and 250,000 viable cells were characterized for each tumor (N = 7). Tumors were collected with informed consent and in accord with the Declaration of Helsinki. Results: This new 35-antibody mass cytometry panel positively identified >99% of GBM cells. Subsets of GBM cells displayed protein expression that matched previously observed transcriptional molecular subclasses (Verhaak et al., Cancer Cell 2010 and TCGA). Strikingly, this panel revealed novel GBM cell subsets defined by contrasting basal signaling profiles. An inverse correlation was observed between baseline STAT3 phosphorylation and the abundance of CD45 + leukocytes. Additionally, similar signaling patterns were seen in cells that expressed proteins associated with distinct functions, such as proliferation and migration. Conclusions: The correlation between low STAT3 signaling and high immune cell abundance provides evidence for the idea that an intimate relationship exists between immune cells and GBM tumor growth and survival. Moreover, single cell analysis may reveal biomarkers of treatment response and allow prediction of clinical outcomes. The abnormal signaling mechanisms observed here in some GBM cell subsets should be studied further as potential targets for novel cancer-selective combination therapies. Citation Format: Nalin Leelatian, Justine Sinnaeve, Bret C. Mobley, Akshitkumar M. Mistry, Daniel Liu, Kyle D. Weaver, Reid C. Thompson, Lola B. Chambless, Rebecca A. Ihrie, Jonathan M. Irish. Mass cytometry of human glioblastoma characterizes more than 99 percent of cells and reveals intratumoral cell subsets defined by contrasting signaling network profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 364. doi:10.1158/1538-7445.AM2017-364

The use of fluorescently-tagged apoptolidins in cellular uptake and response studies

The apoptolidins are glycomacrolide microbial metabolites reported to be selectively cytotoxic against tumor cells. Using fluorescently tagged active derivatives we demonstrate selective uptake of these four tagged glycomacrolides in cancer cells over healthy human blood cells. We also demonstrate the utility of these five fluorescently tagged glycomacrolides in fluorescent flow cytometry to monitor cellular uptake of the six glycomacrolides and cellular response.