Gregor Neuert, Ph.D.

Assistant Professor

gregor.neuert@vanderbilt.edu

Visit Lab Site


Faculty Appointments
Assistant Professor of Molecular Physiology and Biophysics Assistant Professor of Biomedical EngineeringAssistant Professor of Pharmacology
Education
Ph.D., Physics, Ludwig-Maximilians-Universität , Munich, GermanyM.Eng., Ilmenau University of Technology, Ilmenau, Germany
Office Address
813C Light Hall
Research Description
Quantitative systems biology of dynamic signal transduction and gene regulation in single cells.

Many human diseases are caused by cellular and molecular changes in signal transduction and gene regulatory pathways as a consequence of kinetic exposure of humans to harmful environmental conditions. However, when we try to understand molecular alternations that appear in disease cells, almost all biological studies today are performed in environments that are constant over time and may not represent physiologically relevant conditions. In addition, even within a group of individuals diagnosed with the same condition, the disease outcome and response to treatment at the whole body, organ or cellular level can vastly differ. However, because most studies are based on population measurements, they assume that all cells of a specific type behave identical. To address these limitations, my lab aims to understand fundamental mechanisms of signal transduction and gene expression in normal and disease physiology in the context of kinetics and variability. We do so by applying a quantitative framework to broad biological questions in model organisms and in healthy and diseased tissue. We combine a series of single cell, single molecule and genome-wide approaches and complement these quantitative assays with in-depth computational data analysis, genetics, molecular biology, chemical profiling and single cell predictive computational modeling. This combined quantitative experimental and computational framework is the foundation for and expertise of the Neuert lab. Research areas are:

1. Probe signal transduction and gene regulation in physiologically relevant environments: In order to study signal transduction and gene regulatory pathways under physiologically relevant conditions, we have developed a series of methodologies that enables the precise manipulation of environments. To demonstrate the feasibility and biological importance of this approach, we interrogate and control stress response in cells, enabling the manipulation of cellular phenotype, signal transduction and gene regulation. This approach is independent of the biological pathway or organism and presents a general methodology to interrogate and control signal transduction and gene expression pathways. Because many complex diseases are rooted in malfunctioning proteins within signaling and gene networks, improving our ability to define these networks has great potential to lay the foundation to develop a better understanding of cellular pathways as well as better targeted therapies.

2. Understand the function of the noncoding genome: Long non-coding RNAs (lncRNAs) represent a large fraction of the pervasively transcribed genome, although their function is largely unknown. The long-term goal of this area is to understand the effect of lncRNA expression on cellular function, particularly how lncRNAs contribute to gene regulation. In order to better understand the biological relevance of lncRNA, we use the model organisms Saccharomyces cerevisiae and mammalian cells to study the expression dynamics and the regulatory effects of lncRNAs on neighboring mRNAs. To accomplish this, we control environmental conditions precisely and measure the downstream effects on lncRNA and mRNA expression patterns. Our goal is to understand the mechanisms by which lncRNAs regulate gene expression, particularly addressing whether it is the process of lncRNA transcription or the lncRNA transcript itself that is responsible for modulating expression of the neighboring mRNA. Our approach is general and can be applied to any inducible lncRNA or gene regardless of the cell type or organism. We intend to apply the knowledge gained from studying these systems to similar systems that have been implicated in disease in human cells.

3. Revolutionize predictive model identification to gain biological insight: Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes resulting from stochastic gene expression. We are developing optimal experimental design methods to quantify the unique stochastic variability patterns of different biological systems in order to infer and model the relationships between genes within the underlying regulatory networks. Specifically, we are developing sophisticated image processing and experimental approaches to measure spatial-temporal expression patterns of multiple RNA species within the same cell. From these data, we derive multidimensional probability distributions that provide insight into the structure of gene regulatory networks. In essence, these cell-to-cell variability patterns are unique ‘fingerprints’ that reveal information about the relationships between genes within regulatory networks. The benefit of this approach is that the biological system being studied does not have to be genetically manipulated, making it particularly powerful to study mammalian gene regulatory networks.

4. Develop robust and data driven analysis pipelines for kinetic single cell and genomic data sets: Data generated in research directions 1 and 2 are data sets that change over time. In order to analyze these data sets correctly and to extract the maximum amount of biological insight, we develop our own data analysis pipelines. Our focus is on data-driven, robust analysis approaches that make a minimum number of assumptions, are scalable and lead to highly reproducible results. In addition and similar to our ability to generate reproducible data between biological replicas, our data analysis pipelines produce robust results even if done differently on the same data or if done by different individuals at different labs. Examples are our development of single cell signal transduction analysis of time lapse microscopy movies, the counting of long noncoding RNA in single cells or the quantification of nascent transcription in single cells.

Research Keywords
Quantitative Systems Biology of signal transduction and gene regulation of coding and non-coding RNA, big data, bioinformatics, biophysics, chromatin regulation, computational biology, computational modeling, developmental biology, epigenetic regulation, evolution, flow cytometry, gene regulation, genomics, human disease, image processing, immunology, long non-coding RNA, molecular biology, machine learning, quantitative biology, microscopy, pharmacology, RNA, single cells, single molecules, systems biology, signal transduction, transcription, yeast genetics.
Publications
Munsky B, Li G, Fox ZR, Shepherd DP, Neuert G. Distribution shapes govern the discovery of predictive models for gene regulation. Proc. Natl. Acad. Sci. U.S.A [print-electronic]. 2018 Jul 7/17/2018; 115(29): 7533-8. PMID: 29959206, PMCID: PMC6055173, PII: 1804060115, DOI: 10.1073/pnas.1804060115, ISSN: 1091-6490.

Fox Z, Neuert G, Munsky B. Finite state projection based bounds to compare chemical master equation models using single-cell data. J Chem Phys. 2016 Aug 8/21/2016; 145(7): 74101. PMID: 27544081, DOI: 10.1063/1.4960505, ISSN: 1089-7690.

Munsky B, Fox Z, Neuert G. Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics. Methods [print-electronic]. 2015 Sep 9/1/2015; 85: 12-21. PMID: 26079925, PMCID: PMC4537808, PII: S1046-2023(15)00251-0, DOI: 10.1016/j.ymeth.2015.06.009, ISSN: 1095-9130.

Munsky B, Neuert G. From analog to digital models of gene regulation. Phys Biol. 2015 Jul; 12(4): 45004. PMID: 26086470, PMCID: PMC4591055, DOI: 10.1088/1478-3975/12/4/045004, ISSN: 1478-3975.

Neuert G, Munsky B, Tan RZ, Teytelman L, Khammash M, van Oudenaarden A. Systematic identification of signal-activated stochastic gene regulation. Science. 2013 Feb 2/1/2013; 339(6119): 584-7. PMID: 23372015, PMCID: PMC3751578, PII: 339/6119/584, DOI: 10.1126/science.1231456, ISSN: 1095-9203.

van Werven FJ, Neuert G, Hendrick N, Lardenois A, Buratowski S, van Oudenaarden A, Primig M, Amon A. Transcription of two long noncoding RNAs mediates mating-type control of gametogenesis in budding yeast. Cell [print-electronic]. 2012 Sep 9/14/2012; 150(6): 1170-81. PMID: 22959267, PMCID: PMC3472370, PII: S0092-8674(12)00939-7, DOI: 10.1016/j.cell.2012.06.049, ISSN: 1097-4172.

Munsky B, Neuert G, van Oudenaarden A. Using gene expression noise to understand gene regulation. Science. 2012 Apr 4/13/2012; 336(6078): 183-7. PMID: 22499939, PMCID: PMC3358231, PII: 336/6078/183, DOI: 10.1126/science.1216379, ISSN: 1095-9203.

Bumgarner SL, Neuert G, Voight BF, Symbor-Nagrabska A, Grisafi P, van Oudenaarden A, Fink GR. Single-cell analysis reveals that noncoding RNAs contribute to clonal heterogeneity by modulating transcription factor recruitment. Mol. Cell [print-electronic]. 2012 Feb 2/24/2012; 45(4): 470-82. PMID: 22264825, PMCID: PMC3288511, PII: S1097-2765(11)00996-8, DOI: 10.1016/j.molcel.2011.11.029, ISSN: 1097-4164.

Zimmermann JL, Nicolaus T, Neuert G, Blank K. Thiol-based, site-specific and covalent immobilization of biomolecules for single-molecule experiments. Nat Protoc. 2010 Jun; 5(6): 975-85. PMID: 20448543, PII: nprot.2010.49, DOI: 10.1038/nprot.2010.49, ISSN: 1750-2799.

Albrecht CH, Neuert G, Lugmaier RA, Gaub HE. Molecular force balance measurements reveal that double-stranded DNA unbinds under force in rate-dependent pathways. Biophys. J [print-electronic]. 2008 Jun; 94(12): 4766-74. PMID: 18339733, PMCID: PMC2397355, PII: S0006-3495(08)70343-6, DOI: 10.1529/biophysj.107.125427, ISSN: 1542-0086.

Neuert G, Albrecht CH, Gaub HE. Predicting the rupture probabilities of molecular bonds in series. Biophys. J [print-electronic]. 2007 Aug 8/15/2007; 93(4): 1215-23. PMID: 17468164, PMCID: PMC1929050, PII: S0006-3495(07)71379-6, DOI: 10.1529/biophysj.106.100511, ISSN: 0006-3495.

Neuert G, Albrecht C, Pamir E, Gaub HE. Dynamic force spectroscopy of the digoxigenin-antibody complex. FEBS Lett [print-electronic]. 2006 Jan 1/23/2006; 580(2): 505-9. PMID: 16388805, PII: S0014-5793(05)01535-8, DOI: 10.1016/j.febslet.2005.12.052, ISSN: 0014-5793.

Available Postdoctoral Position Details
Posted Position
10/15/2018

Postdoctoral Fellow Position in Single Cell Cancer Biology

A Postdoctoral Fellow position is immediately available for a highly motivated upcoming or recent Ph.D. or M.D./Ph.D. graduate in Quantitative Biology, Biomedical Engineering, Chemical Engineering, Molecular Biology or related biological oriented field in the lab of Dr. Gregor Neuert, NIH Director’s New Innovator Awardee 2014, at Vanderbilt University, School of Medicine in Nashville, Tennessee.

The Neuert lab is part of the NCI - Pre-Cancer Atlas Consortium where we aim to apply our novel single molecule RNA-FISH assay to distinguish between individual normal and pre-cancerous cells. Our research methods include a combination of single-molecule microscopy / single-cell experiments, and quantitative image processing.

As part of the Pre-Cancer Atlas Consortium and in collaboration with the Lau Lab at Vanderbilt, we are seeking a highly motivated, driven and curious applicant (recent graduate with no more than 1 year of previous postdoctoral training), ideally with a rich set of skills in molecular biology, quantitative microscopy, image processing and a strong background, proven record (first author publications) and rigorous training in one of the following areas: biomedical engineering, chemical engineering, quantitative biology, biophysics, cell biology or molecular biology.

The ideal candidate will combine his/her biology or engineering training with quantitative approaches developed in the Neuert and the Lau lab’s to establish a multiplexed RNA-FISH platform in human patient samples based on our single molecule RNA-FISH expertise (van Werven Cell, 2012; Bumgarner Mol Cell 2012; Munsky Science 2012; Neuert Science 2013; Li bioRxiv 2017; Munsky PNAS 2018). The ideal candidate should have basic quantitative training including basic computer programing.

We offer training in single cell biology, quantitative biology, computer programing, complex data analysis, novel experimental design, in addition to an exciting, highly interactive, international, interdisciplinary, and well-funded research environment with strong mentoring and career development support.

Vanderbilt University School of Medicine is a top ranked medical school and is equipped with world-class cutting-edge experimental and computational core facilities. With a metro population of approximately 1.5 million people, Nashville offers high quality music, great restaurants, rapid access to outdoor activities and is centrally located within the US.

Interested applicants should send a curriculum vita, a summary of research experience and accomplishments, and contact information of 2-3 references to gregor.neuert@vanderbilt.edu. Applicants should explain briefly why they are interested in pursuing a postdoctoral position in general and why in the Neuert lab.

For more information please visit: https://lab.vanderbilt.edu/neuert-lab/ 


12/21/2017

Postdoctoral Fellow Position in Cell Biology

A Postdoctoral Fellow position is immediately available for a highly motivated upcoming or recent Ph.D. or M.D./Ph.D. graduate in Cell Biology or related biological field in the lab of Dr. Gregor Neuert, NIH Director’s New Innovator Awardee 2014, at Vanderbilt University, School of Medicine in Nashville, Tennessee.

The Neuert lab works on the leading edge in the quantitative understanding of molecular mechanism contributing to the function and malfunction of signal transduction and gene regulatory processes in yeast and mammalian cells. Our research methods include a combination of single-molecule / single-cell experiments, next generation sequencing, predictive computational modeling, molecular biology and genetics.

We are seeking highly motivated, driven and curious applicants (recent graduate with no more than 1 year of previous postdoctoral training), a rich set of skills in mammalian cell culture and mammalian cell biology techniques with a focus on signaling or transcription, a strong background, proven record (first author publications) and rigorous training in one of the following areas: stem cell biology, cancer biology, immunology, pharmacology, developmental biology, or neuroscience.

The ideal candidate will combine his/her cell biological training with quantitative approaches from the Neuert lab to ask fundamental question related to how environmental changes over time impact cellular phenotypes, cell signaling and gene expression in single cells (see Li, Neuert, bioRxiv, 2017). The ideal candidate should have some basic quantitative training or is expected to learn computer programing.

We offer training in single cell biology, quantitative biology, computer programing, complex data analysis, novel experimental design, in addition to an exciting, highly interactive, international, interdisciplinary, and well-funded research environment with strong mentoring and career development support.

Vanderbilt University School of Medicine is ranked #8 in NIH funding, #14 in U.S. News Medical School Ranking, #25 in U.S. News Biomedical Engineering Ranking, #3 in Physiology and is equipped with world-class cutting-edge experimental and computational core facilities. Nashville is a mecca for high quality music, great restaurants, rapid access to outdoor activities and is centrally located within the US.

Interested applicants should send a curriculum vita, a summary of research experience and accomplishments, and contact information of 2-3 references to gregor.neuert@vanderbilt.edu. Applicants should explain briefly why they are interested in pursuing a postdoctoral position in general and why in the Neuert lab.

For more information please visit: https://medschool.vanderbilt.edu/neuert-lab