Peng Qiu

Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum

Simultaneous measurement of more than 30 properties in individual human cells is used to characterize signaling in the immune system. Flow cytometry is an essential tool for dissecting the functional complexity of hematopoiesis. We used single-cell “mass cytometry” to examine healthy human bone marrow, measuring 34 parameters simultaneously in single cells (binding of 31 antibodies, viability, DNA content, and relative cell size). The signaling behavior of cell subsets spanning a defined hematopoietic hierarchy was monitored with 18 simultaneous markers of functional signaling states perturbed by a set of ex vivo stimuli and inhibitors. The data set allowed for an algorithmically driven assembly of related cell types defined by surface antigen expression, providing a superimposable map of cell signaling responses in combination with drug inhibition. Visualized in this manner, the analysis revealed previously unappreciated instances of both precise signaling responses that were bounded within conventionally defined cell subsets and more continuous phosphorylation responses that crossed cell population boundaries in unexpected manners yet tracked closely with cellular phenotype. Collectively, such single-cell analyses provide system-wide views of immune signaling in healthy human hematopoiesis, against which drug action and disease can be compared for mechanistic studies and pharmacologic intervention.

Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE

The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.

TCGA-assembler: open-source software for retrieving and processing TCGA data.

To the Editor: The Cancer Genome Atlas (TCGA) has been generating multi-modal genomics, epigenomics, and proteomics data for thousands of tumor samples across more than 20 types of cancer. While the access to most level-1 and -2 TCGA data is restricted, the entire level-3 TCGA data as well as some level-1 clinical data (e.g., survival and drug treatments) are publicly available. Included in the public data are genome-wide measurements of different genetic characterizations, such as DNA copy number, DNA methylation, and mRNA expression for the same genes, providing unprecedented opportunities for systematic investigation of cancer mechanisms at multiple molecular and regulatory layers [1-3]. Few tools of integrative data mining for TCGA are present, partly due to lack of tools to acquire and assemble the large scale TCGA data. Specifically, the level-3 TCGA data are stored as hundreds of thousands of sample- and platform-specific files, accessible through HTTP directories on the servers of TCGA Data Coordinating Center (DCC) [4]. Navigating through all of the files manually is impossible. Although Firehose [5] nicely assemble and publish TCGA data, it does not share the program code for data assembly. Currently the community does not have access to open-source data retrieving tools for automatic and flexible data acquisition, hence severely hindering the progress in systemic data integration and reproducible computational analysis using TCGA data. To meet these challenges, we introduce TCGA-Assembler, a software package that automates and streamlines the retrieval, assembly, and processing of public TCGA data. TCGA-Assembler equips users the ability to produce Firehose-type of TCGA data, with open-source and freely available program script. TCGA-Assembler opens a door for the development of data-mining and data-analysis tools that generate fully reproducible results, including data acquisition. TCGA-Assembler consists of two modules (Fig. 1a), both written in R (http://www.r-project.org). Module A streamlines data downloading and quality check, and module B processes the downloaded data for subsequent analyses (Supplementary Methods). In particular, module A takes advantage of the informative naming mechanism of TCGA data file system (Supplementary Fig. 1) and applies a recursive algorithm to retrieve the URLs of all data files. By string matching on the URLs, module A allows users to download most of TCGA public data (Supplementary Table 1) across genomic features and cancer types. For each genomics feature (such as gene expression from RNA-Seq) a data matrix combining multiple samples (Fig. 1b) is produced, with rows representing genomics units (such as genes) and columns representing samples. Module B provides convenient and important data preprocessing functions, such as mega-data assembly, data cleaning, and quantification of various measurements. For users interested in integrative analysis [6], a mega data matrix (Fig. 1c) is required that matches different types of genomics measurements for the same genes across samples. Module B provides a function “CombineMultiPlatfomData” to fulfill this requirement (Supplementary Methods), which involves intricate data-matching steps to overcome the feature-labeling discrepancies caused by different lab protocols and biotechnologies in the experiments. Other data-processing functions are also provided to facilitate downstream analysis (Supplementary Methods). Figure 1 TCGA-Assembler as a tool for acquiring, assembling, and processing public TCGA data. (a) Flowchart of TCGA- Assembler. Module A acquires data from TCGA DCC. Module B processes the obtained data using various functions. (b) Illustration of a data matrix ... Other big data tools for TCGA are available [5, 7, 8]. In particular, level-3 TCGA data can also be obtained from Firehose [5] at the MIT Broad Institute in the same format as in Fig. 1b, one for each cancer type and genomics platform. Module A of TCGA-Assembler not only provides the same type of data matrices, but also distributes R functions and associated computer program that produce the data matrices. Equipped with the open-source tool, users will be independent and control what and when TCGA data will be acquired locally. More importantly, quantitatively advanced users may integrate our open-source programs with downstream data analysis tools to realize reproducible and automated data analysis for TCGA. Unique to TCGA-Assembler is module B that provides critical functions for data cleaning and processing. For example, the mega data table (Fig. 1c) can be obtained with a single function, behind which substantial efforts have been directed to ensure the validity of process, such as to check and correct gene symbol discrepancies. Lastly, TCGA-Assembler is fully compatible with Firehose in that the data processing functions in Module B can directly process data files downloaded from Firehose. This compatibility is crucial to those who want to take advantage of both software pipelines. TCGA-Assembler will remain freely available and open-source. In the future, more data processing and analysis functions will be continuously added to TCGA-Assembler based on user feedback and new research needs. The authors request acknowledgment of the use of TCGA-Assembler in published works.

COSMID: A Web-based Tool for Identifying and Validating CRISPR/Cas Off-target Sites.

Precise genome editing using engineered nucleases can significantly facilitate biological studies and disease treatment. In particular, clustered regularly interspaced short palindromic repeats (CRISPR) with CRISPR-associated (Cas) proteins are a potentially powerful tool for modifying a genome by targeted cleavage of DNA sequences complementary to designed guide strand RNAs. Although CRISPR/Cas systems can have on-target cleavage rates close to the transfection rates, they may also have relatively high off-target cleavage at similar genomic sites that contain one or more base pair mismatches, and insertions or deletions relative to the guide strand. We have developed a bioinformatics-based tool, COSMID (CRISPR Off-target Sites with Mismatches, Insertions, and Deletions) that searches genomes for potential off-target sites (http://crispr.bme.gatech.edu). Based on the user-supplied guide strand and input parameters, COSMID identifies potential off-target sites with the specified number of mismatched bases and insertions or deletions when compared with the guide strand. For each site, amplification primers optimal for the chosen application are also given as output. This ranked-list of potential off-target sites assists the choice and evaluation of intended target sites, thus helping the design of CRISPR/Cas systems with minimal off-target effects, as well as the identification and quantification of CRISPR/Cas induced off-target cleavage in cells.

PSGL-1/selectin and ICAM-1/CD18 interactions are involved in macrophage-induced drug resistance in myeloma

Chemoresistance is the major obstacle in multiple myeloma (MM) management. We previously showed that macrophages protect myeloma cells, on a cell contact basis, from melphalan or dexamethasone-induced apoptosis in vitro. In this study, we found that macrophage-mediated myeloma drug resistance was also seen with purified macrophages from myeloma patients’ bone marrow (BM) in vitro and was confirmed in vivo using the human myeloma-SCID (severe combined immunodeficient) mouse model. By profiling differentially regulated and paired plasma membrane protein genes, we showed that PSGL-1 (P-selectin glycoprotein ligand-1)/selectins and ICAM-1/CD18 played an important role in macrophage-mediated myeloma cell drug resistance, as blocking antibodies against these molecules or genetic knockdown of PSGL-1 or ICAM-1 in myeloma cells repressed macrophages’ ability to protect myeloma cells. Interaction of macrophages and myeloma cells via these molecules activated Src and Erk1/2 kinases and c-myc pathways and suppressed caspase activation induced by chemotherapy drugs. Thus, our study sheds new light on the mechanism of drug resistance in MM and provides novel targets for improving the efficacy of chemotherapy in patients.

Fast calculation of pairwise mutual information for gene regulatory network reconstruction

We present a new software implementation to more efficiently compute the mutual information for all pairs of genes from gene expression microarrays. Computation of the mutual information is a necessary first step in various information theoretic approaches for reconstructing gene regulatory networks from microarray data. When the mutual information is estimated by kernel methods, computing the pairwise mutual information is quite time-consuming. Our implementation significantly reduces the computation time. For an example data set of 336 samples consisting of normal and malignant B-cells, with 9563 genes measured per sample, the current available software for ARACNE requires 142 hours to compute the mutual information for all gene pairs, whereas our algorithm requires 1.6 hours. The increased efficiency of our algorithm improves the feasibility of applying mutual information based approaches for reconstructing large regulatory networks.

Single-cell mass cytometry reveals intracellular survival/proliferative signaling in FLT3-ITD-mutated AML stem/progenitor cells.

Understanding the unique phenotypes and complex signaling pathways of leukemia stem cells (LSCs) will provide insights and druggable targets that can be used to eradicate acute myeloid leukemia (AML). Current work on AML LSCs is limited by the number of parameters that conventional flow cytometry (FCM) can analyze because of cell autofluorescence and fluorescent dye spectral overlap. Single‐cell mass cytometry (CyTOF) substitutes rare earth elements for fluorophores to label antibodies, which allows measurements of up to 120 parameters in single cells without correction for spectral overlap. The aim of this study was the evaluation of intracellular signaling in antigen‐defined stem/progenitor cell subsets in primary AML. CyTOF and conventional FCM yielded comparable results on LSC phenotypes defined by CD45, CD34, CD38, CD123, and CD99. Intracellular phosphoprotein responses to ex vivo cell signaling inhibitors and cytokine stimulation were assessed in myeloid leukemia cell lines and one primary AML sample. CyTOF and conventional FCM results were confirmed by western blotting. In the primary AML sample, we investigated the cell responses to ex vivo stimulation with stem cell factor and BEZ235‐induced inhibition of PI3K and identified activation patterns in multiple PI3K downstream signaling pathways including p‐4EBP1, p‐AKT, and p‐S6, particularly in CD34+ subsets. We evaluated multiple signaling pathways in antigen‐defined subpopulations in primary AML cells with FLT3‐ITD mutations. The data demonstrated the heterogeneity of cell phenotype distribution and distinct patterns of signaling activation across AML samples and between AML and normal samples. The mTOR targets p‐4EBP1 and p‐S6 were exclusively found in FLT3‐ITD stem/progenitor cells, but not in their normal counterparts, suggesting both as novel targets in FLT3 mutated AML. Our data suggest that CyTOF can identify functional signaling pathways in antigen‐defined subpopulations in primary AML, which may provide a rationale for designing therapeutics targeting LSC‐enriched cell populations.

Visualization and cellular hierarchy inference of single-cell data using SPADE

High-throughput single-cell technologies provide an unprecedented view into cellular heterogeneity, yet they pose new challenges in data analysis and interpretation. In this protocol, we describe the use of Spanning-tree Progression Analysis of Density-normalized Events (SPADE), a density-based algorithm for visualizing single-cell data and enabling cellular hierarchy inference among subpopulations of similar cells. It was initially developed for flow and mass cytometry single-cell data. We describe SPADEs implementation and application using an open-source R package that runs on Mac OS X, Linux and Windows systems. A typical SPADE analysis on a 2.27-GHz processor laptop takes ∼5 min. We demonstrate the applicability of SPADE to single-cell RNA-seq data. We compare SPADE with recently developed single-cell visualization approaches based on the t-distribution stochastic neighborhood embedding (t-SNE) algorithm. We contrast the implementation and outputs of these methods for normal and malignant hematopoietic cells analyzed by mass cytometry and provide recommendations for appropriate use. Finally, we provide an integrative strategy that combines the strengths of t-SNE and SPADE to infer cellular hierarchy from high-dimensional single-cell data.

CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data

MOTIVATION Recent advances in flow cytometry enable simultaneous single-cell measurement of 30+ surface and intracellular proteins. CytoSPADE is a high-performance implementation of an interface for the Spanning-tree Progression Analysis of Density-normalized Events algorithm for tree-based analysis and visualization of this high-dimensional cytometry data. AVAILABILITY Source code and binaries are freely available at http://cytospade.org and via Bioconductor version 2.10 onwards for Linux, OSX and Windows. CytoSPADE is implemented in R, C++ and Java. CONTACT michael.linderman@mssm.edu SUPPLEMENTARY INFORMATION Additional documentation available at http://cytospade.org.

Thinking Outside the Gate: Single-Cell Assessments in Multiple Dimensions

Present address: Immuno-Oncology & Combinations DPU, Oncology RDBendall et al., 2011), allowing an oppor-tunity to better understand the immuno-logical mechanisms underlying disease.Complex flow cytometry (FCM) data arenow surpassing our ability to fully analyzeand interpret all information via currentstandard approaches, such as 2D dotplots and Boolean gates. Indeed, thenumber of potential cell subpopulationsincreases exponentially with the numberof parameters assessed, making it diffi-cult to decipher all possible combina-tions included in the raw data (e.g., 512potential subsets with nine markers) viathe traditional approaches (Bendall andNolan, 2012). This could limit the transla-tion of technical advances into new diag-nostics or therapies. Newly developedbioinformatics tools that have the poten-tial to bridge this gap are now available.The aim of this letter is to foster theimplementation and adoption of thesenovel computational methodologies forunbiased analysis of complex cytometrydata.In recent years, a host of new data-analysis tools have emerged, creatingworkflows for processing and analyzingcomplex FCM datasets; however, thesehave gone mostly unnoticed by immunol-ogists. Table S1 provides an overview ofmany of the currently available tools andtheir specific applications. They can beassigned to specific categories arrangedin a ‘‘FCM data-analysis workflow’’ fromcompensated data as input to biologi-cally interpretable results as output. Thevast majority of the listed tools for FCMdata processing, analysis, and visualiza-tion are made available by the bio-informaticians at no cost and includeopen source code and unrestrictive soft-ware licensing, opening up these compu-tational approaches to broad use by theresearch community. Many of the toolshave been developed to address similaranalysis objectives via quite different ap-proaches. They might provide optimal re-sultsfordifferentdatasets,suchthatthereis no ‘‘right’’ or ‘‘best’’ tool, and usingseveral algorithms in combination mightyield even better results and exceedthe possibilities offered by manual anal-ysis.Comprehensivecomparativestudiesby the Flow Cytometry: Critical Assess-mentofPopulationIdentificationMethods(FlowCAP) project have shown that manyof these tools have reached a level ofmaturitythatmatches,orevensurpasses,the results produced by human experts(Aghaeepour et al., 2013).The development of computational ap-proaches addresses many needs asso-ciated with high-dimensional datasets.However,fortheimmunologycommunity,threemain challengeshave surfaced, andtackling them will facilitate a paradigmshift in the analysis of FCM data. First,despite the focused efforts by bio-informaticians to develop novel tools foranalyzingFCMdata,onlyaminorityofim-munologists are aware of the advantagesofferedtothefield.Thesetoolsneedtobepresented in immunology forums ratherthan limited to bioinformatics journalsand conferences. Second, even thoughthe vast majority of the computationalImmunity 42, April 21, 2015 a2015 Elsevier Inc. 591