El-ad D. Amir
Columbia University
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Featured researches published by El-ad D. Amir.
Science | 2011
Sean C. Bendall; Erin F. Simonds; Peng Qiu; El-ad D. Amir; Peter O. Krutzik; Rachel Finck; Robert V. Bruggner; Rachel D. Melamed; Angelica Trejo; Olga Ornatsky; Robert S. Balderas; Sylvia K. Plevritis; Karen Sachs; Dana Pe’er; Scott D. Tanner; Garry P. Nolan
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.
Nature Biotechnology | 2013
El-ad D. Amir; Kara L. Davis; Michelle D. Tadmor; Erin F. Simonds; Jacob H. Levine; Sean C. Bendall; Daniel K. Shenfeld; Smita Krishnaswamy; Garry P. Nolan; Dana Pe'er
New high-dimensional, single-cell technologies offer unprecedented resolution in the analysis of heterogeneous tissues. However, because these technologies can measure dozens of parameters simultaneously in individual cells, data interpretation can be challenging. Here we present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the high-dimensional structure of the data. viSNE plots individual cells in a visual similar to a scatter plot, while using all pairwise distances in high dimension to determine each cells location in the plot. We integrated mass cytometry with viSNE to map healthy and cancerous bone marrow samples. Healthy bone marrow automatically maps into a consistent shape, whereas leukemia samples map into malformed shapes that are distinct from healthy bone marrow and from each other. We also use viSNE and mass cytometry to compare leukemia diagnosis and relapse samples, and to identify a rare leukemia population reminiscent of minimal residual disease. viSNE can be applied to any multi-dimensional single-cell technology.
Cell | 2015
Jacob H. Levine; Erin F. Simonds; Sean C. Bendall; Kara L. Davis; El-ad D. Amir; Michelle D. Tadmor; Oren Litvin; Harris G. Fienberg; Astraea Jager; Eli R. Zunder; Rachel Finck; Amanda Larson Gedman; Ina Radtke; James R. Downing; Dana Pe’er; Garry P. Nolan
Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology.
Proceedings of the National Academy of Sciences of the United States of America | 2015
Yao Lu; Qiong Xue; Markus R. Eisele; Endah S. Sulistijo; Kara Brower; Lin Han; El-ad D. Amir; Dana Pe’er; Kathryn Miller-Jensen; Rong Fan
Significance We demonstrated codetection of 42 immune effector proteins in single cells, representing the highest multiplexing recorded to date for a single-cell secretion assay. Using this platform to profile differentiated macrophages stimulated with lipopolysaccharide reveals previously unobserved deep functional heterogeneity and varying levels of pathogenic activation, which is conserved throughout the cell activation process and prevails as it is extended to other Toll-like receptor (TLR) ligands and to primary human macrophages. The results indicate that the phenotypically similar cell population could still exhibit a large degree of intrinsic heterogeneity at the cell function level. This technology enables full-spectrum dissection of immune functional states in response to pathogenic stimulation and allows for more comprehensive and accurate monitoring of cellular immunity. Despite recent advances in single-cell genomic, transcriptional, and mass-cytometric profiling, it remains a challenge to collect highly multiplexed measurements of secreted proteins from single cells for comprehensive analysis of functional states. Herein, we combine spatial and spectral encoding with polydimethylsiloxane (PDMS) microchambers for codetection of 42 immune effector proteins secreted from single cells, representing the highest multiplexing recorded to date for a single-cell secretion assay. Using this platform to profile differentiated macrophages stimulated with lipopolysaccharide (LPS), the ligand of Toll-like receptor 4 (TLR4), reveals previously unobserved deep functional heterogeneity and varying levels of pathogenic activation. Uniquely protein profiling on the same single cells before and after LPS stimulation identified a role for macrophage inhibitory factor (MIF) to potentiate the activation of LPS-induced cytokine production. Advanced clustering analysis identified functional subsets including quiescent, polyfunctional fully activated, partially activated populations with different cytokine profiles. This population architecture is conserved throughout the cell activation process and prevails as it is extended to other TLR ligands and to primary macrophages derived from a healthy donor. This work demonstrates that the phenotypically similar cell population still exhibits a large degree of intrinsic heterogeneity at the functional and cell behavior level. This technology enables full-spectrum dissection of immune functional states in response to pathogenic or environmental stimulation, and opens opportunities to quantify deep functional heterogeneity for more comprehensive and accurate immune monitoring.
Cell | 2014
Sean C. Bendall; Kara L. Davis; El-ad D. Amir; Michelle D. Tadmor; Erin F. Simonds; Tiffany J. Chen; Daniel K. Shenfeld; Garry P. Nolan; Dana Pe’er
Archive | 2013
Dana Pe'er; El-ad D. Amir
Cancer Research | 2013
Erin F. Simonds; Sean C. Bendall; Amanda Larson Gedman; Jacob H. Levine; Kara L. Davis; Harris G. Fienberg; Astraea Jager; El-ad D. Amir; Ina Radtke; Wendy J. Fantl; Dana Pe'er; James R. Downing; Garry P. Nolan
Cancer Research | 2013
El-ad D. Amir; Oren Litvin; Jacob H. Levine; Sean C. Bendall; Kara L. Davis; Erin F. Simonds; Tanya Schild; Mark Rocco; Neal Rosen; Garry P. Nolan; Dana Pe'er
Blood | 2012
Sean C. Bendall; El-ad D. Amir; Michelle D. Tadmor; Kara L. Davis; Erin F. Simonds; Daniel K. Shenfeld; Jacob H. Levine; Garry P. Nolan; Dana Pe'er
Blood | 2012
Kara L. Davis; Sean C. Bendall; El-ad D. Amir; Erin F. Simonds; Astraea Jager; Angelica Trejo; Dana Pe'er; Garry P. Nolan