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Garry Nolan
Email:
Phone:(650) 725-7002 Profile: http://med.stanford.edu/profiles/Garry_Nolan/
Alternate Contact: Academic Appointments
Appointment
Organization
Associate Professor
Member
Member
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Curriculum Vitae PDF
Honors & Awards
Title
Organization
Date(s)
Stohlman Scholar
Leukemia and Lymphoma Society
2000
Scholar of the Leukemia Society
Leukemia and Lymphoma Society
1996-2000
Burrough's Wellcome Investigator's Award In Pharmacology
Burroughs Wellcome
1995-2000
Administrative Appointments
Title
Organization
Start Year
End Year
Director, Stanford NHLBI Proteomics Center
National Heart, Lung, and Blood Institute of the NIH
2002
2009
Professional Education
Degree
Awarding Institution
Field of Study
Year of Graduation
B.S.
Cornell University
Genetics
1983
Ph.D.
Stanford University
Genetics
1989
Postdoctoral Fellowship
MIT, David Baltimore Laboratory
Biochemistry
1993
Postdoctoral Advisees
Sean Bendall,
Matthew Hale,
Faye Hsu,
Nikesh Kotecha,
Shigeru Okumura,
Jason Ptacek,
Karen Sachs
Web Site Links
Research/Lab website:
Nolan Lab
Research Interests
Control of T cell signaling, machine learning of signaling states by systems biology, and leukemia/cancer autoimmunity are prominent in our studies. We use advanced Flow Cytometric analysis (FACS) of phosphoproteins in single cells to achieve many of our goals. Signaling systems can now be analyzed directly by flow cytometry and Fluorescence Activated Cell Sorting, primarily through technologies developed in our laboratory, focused on following multiple phosphoproteins in complex populations of primary cells such as mouse cells and even clinical samples. Up to 15 simultaneous parameters can be followed in single cells including multiple kinases, phosphoproteins, cell cycle proteins, and other parameters, enabling resolution of cellular activation states.
We are using these techniques to study B and T cell signaling, dendritic cell function, and other immune parameters by analysis of biochemical functions at the single cell level. Recently, we have begun using the approach to distinguish predictive patterns of intracellular signaling to classify patient responses to chemotherapies and to determine how their signaling systems are altered in disease states. We are also using the technique for drug screening in primary cells to truly select for drugs with efficacies in certain cell subsets but not others.
Autoimmune diseases in which we have particular interest include rheumatoid arthritis and systemic lupus erythematosus. In these diseases, we focus on understanding how the immune system becomes dysregulated as disease comes and goes. We can measure and determine the cellular network states in multiple cell subsets. In cancer, we are working in follicular lymphoma as well as acute myelogenous leukemia where we can look at disease progression as a measure of changes in disease states correlated to particular genetic changes in the genome of human cancer cells. Also we have made determined efforts in understanding how the micro-environment of cancers modulates immune signaling. We have made significant strides in understanding this in several solid cancer models and have begun working with human clinical samples.
We put substantial effort into bioinformatics approaches to mine the datasets we collect and to automate the production of network models of the signaling pathways affected. For this, we have collaborations with statisticians, engineering departments, and computer design specialists here at Stanford and UC Berkeley to extend our efforts to make the program in the laboratory extremely cross-disciplinary.
We are using these techniques to study B and T cell signaling, dendritic cell function, and other immune parameters by analysis of biochemical functions at the single cell level. Recently, we have begun using the approach to distinguish predictive patterns of intracellular signaling to classify patient responses to chemotherapies and to determine how their signaling systems are altered in disease states. We are also using the technique for drug screening in primary cells to truly select for drugs with efficacies in certain cell subsets but not others.
Autoimmune diseases in which we have particular interest include rheumatoid arthritis and systemic lupus erythematosus. In these diseases, we focus on understanding how the immune system becomes dysregulated as disease comes and goes. We can measure and determine the cellular network states in multiple cell subsets. In cancer, we are working in follicular lymphoma as well as acute myelogenous leukemia where we can look at disease progression as a measure of changes in disease states correlated to particular genetic changes in the genome of human cancer cells. Also we have made determined efforts in understanding how the micro-environment of cancers modulates immune signaling. We have made significant strides in understanding this in several solid cancer models and have begun working with human clinical samples.
We put substantial effort into bioinformatics approaches to mine the datasets we collect and to automate the production of network models of the signaling pathways affected. For this, we have collaborations with statisticians, engineering departments, and computer design specialists here at Stanford and UC Berkeley to extend our efforts to make the program in the laboratory extremely cross-disciplinary.
Publications
- Perez OD, Mitchell D, Nolan GP "Differential role of ICAM ligands in determination of human memory T cell differentiation." BMC Immunol 2007; 8: 2 More »
- Nolan GP, "What's wrong with drug screening today." Nat Chem Biol 2007; 3: 4: 187-91 More »
- Van Meter ME, Díaz-Flores E, Archard JA, Passegué E, Irish JM, Kotecha N, Nolan GP, Shannon K, Braun BS "K-RasG12D expression induces hyperproliferation and aberrant signaling in primary hematopoietic stem/progenitor cells." Blood 2007; 109: 9: 3945-52 More »
- Krutzik PO, Crane JM, Clutter MR, Nolan GP "High-content single-cell drug screening with phosphospecific flow cytometry." Nat Chem Biol 2007; More »
- Bjørndal B, Myklebust LM, Rosendal KR, Myromslien FD, Lorens JB, Nolan G, Bruland O, Lillehaug JR "RACK1 regulates Ki-Ras-mediated signaling and morphological transformation of NIH 3T3 cells." Int J Cancer 2007; 120: 5: 961-9 More »
123 publications: view full list
