The Chuang Lab

My lab uses computational, mathematical, and high-throughput data generation approaches to study how cancer ecosystems function, evolve, and respond to therapeutic treatment. For an up-to-date description, please see the Chuanglab personal site: chuanglabwiki.jax.org.

Cancer Evolution and Patient-Derived Xenografts

The Chuang lab is an international leader in patient-derived xenografts, a model system in which human tumors are engrafted and studied in mice. Xenografts play a critical role in cancer research, as they are often used in therapeutic testing just prior to clinical trials. Since 2017, our lab has led the Data Coordination Center for the NCI PDXNet, a multi-institute consortium supported by the NCI Cancer Moonshot Initiative. We are partnering with multiple academic teams, industry partners, and the NCI to develop and study almost 1000 new xenograft models across diverse cancer types. In collaboration with colleagues across the US, Europe, and Asia, we have used xenografts for studies on the genetic drivers of cancer and drug resistance, as well as the robustness of xenografts for preclinical testing (Woo, Giordano et al 2021, Kim et al 2018, Noorbakhsh and Chuang 2017) Evard et al 2020).

Cancer Image Analysis

Cancer treatment response is affected by tumor cell heterogeneity, immune cells, and other microenvironmental features (Noorbakhsh, Zhao, Russell, and Chuang 2020). To understand this web of interactions, the lab uses computational image analysis to reveal the spatial processes in tumors. We have developed convolutional neural networks that can be applied to H&E images to accurately distinguish tumor regions, identify cancer subtypes, and classify mutation status (Noorbakhsh, Farahmand, Foroughi Pour et al 2020). Critically, our work has shown that images from diverse cancer types can be combined to enhance discovery from large image sets. The lab analyzes many other types of imaging data as well, including confocal microscopy, imaging mass cytometry, and spatial transcriptomics across tumors, organoids, and xenografts, in close collaboration with cancer biologists, oncologists and pathologists.

RNA-level Gene Regulation

A secondary interest of the lab is mammalian gene regulation at the RNA level, which stem from our longtime experience in computational analysis of selection pressures on RNA (Kural et al 2009, Dotu et al 2018). For example, our team performed the computational analysis for the first study showing a tRNA mutation as the driver for a mammalian phenotype (Ishimura et al 2014). We continue to study translational dysregulation mechanisms, particularly in the context of neurodegeneration (Kapur et al 2020, Terrey et al 2020)

Chromatin interactions and its 3D structure play a major role in regulating gene expression by bringing together regulatory elements that are distal on the linear genome in close physical proximity with each other. Investigators at the JAX 4D Nucleome Center at The Jackson Laboratory for Genomic Medicine in Farmington, CT have pioneered genomic technologies for genome-wide profiling chromatin interactions. The JAX 4D Nucleome Center is seeking enthusiastic, highly-skilled computational and experimental post-docs to generate and analyze human chromatin interaction datasets in integration with other genomics and genetics datasets. 

Job Description 

Our center is seeking multiple postdoctoral researchers to develop computational and genomic approaches for understanding 3D genome biology under the light of diverse genomic and genetic information. We are seeking self-motivated, independent individuals who are interested in building their own career paths; however, postdoctoral fellows are expected to coordinate with and report to the PI responsible for the research area(s) addressed by their project. The fellows will develop research projects in one or more areas of focus: 

• 3D genome mapping and imaging technologies 
• Application of nano-engineering toward genomic methodologies 
• Computational modeling of 3D genome architectures 
• Building regulatory networks at multiple layers including gene expression, epigenetic, and chromatin data 
• Integrating regulatory interactions with genetic data 
• Mining and aligning chromatin interaction datasets in diverse human cell types 
• Functional perturbation of topological elements 

Desired Candidate Attributes 

The JAX 4D Nucleome Center is interdisciplinary and collaborative, and preference will be given to individuals with experience working in such an environment. 

For computational postdocs desired attributes include: 

• Strong problem-solving skills, creative thinking, and enthusiastic about science 
• Excellent communication skills and fully fluent spoken and written English 
• Familiarity with next generation sequencing data sets and pre-processing 
• PhD in a statistics/biostatistics or another computational field (CS, EE, Physics) with a strong foundation in statistics and data analyses 
• Excellent programming skills, preferentially with expertise in R and/or python and UNIX programming 
• Familiarity with network building, mining, alignment algorithms 
• Familiarity with bioinformatics resources, i.e., UCSC genome browser, NCBI.

For experimental postdocs desired attributes include: 

• Strong problem-solving skills, creative thinking, and enthusiastic about science 
• Excellent communication skills and fully fluent spoken and written English 
• Familiarity with next generation sequencing data sets and data generation 
• PhD in a genomics/genetics/biology or another relevant field 
• Expertise in cellular and molecular biology techniques 

Application Package 

Applications can be submitted to email address JAX4DN@jax.org as a single pdf document that contains: 1) a cover letter, 2) a full CV with a complete list of publications, 3) contact information for three references, and 4) a list of laboratory methods and computational skills mastered.

JAX 4D Nucleome Center Description 

Although the genome is typically thought of as a linear sequence, it is actually a dynamic three-dimensional structure. Gene loci and regulatory elements that are linearly distant—or even on separate chromosomes—may be brought in spatial proximity, and such interactions are of fundamental importance for understanding genome regulation. The ultimate goal of the JAX 4D Nucleome Center is to deliver a Nucleome Positioning System for the generation of complex chromatin interaction network maps in the context of 3D genome structures. Such maps will be used to monitor and reference the dynamics of individual genomic elements, providing context to better understand gene function and the effects of genetic variation on gene function. The scientific team is led by Yijun Ruan, Chia-Lin Wei, Jeff Chuang, and Duygu Ucar. 

The four major scientific goals of the JAX 4D Nucleome Center are to: 

• Develop and improve technologies for high-resolution and haplotype-specific 3D mapping. 
• Refine an integrative and comprehensive data-analysis platform (3D-NOME) for the study of nuclear architecture. 
• Elucidate the spatiotemporal regulation of 3D genome organization and function in human cells. 
• Use nuclear imaging and genome editing to structurally and functionally validate the 3D maps and predictions of regulatory interactions developed by the center.

The Jackson Laboratory Description 

The Jackson Laboratory (JAX) is an independent, non-profit organization focusing on mammalian and human genomics research to advance human health. The mission of its newest institute, The Jackson Laboratory for Genomic Medicine (JAX-GM), is to discover the precise genomic causes of disease and develop individualized diagnostics, treatments and cures by merging the Laboratory’s eight decades of research in mammalian genetics with those of our JAX-GM faculty and with the expertise of our clinical partners in Connecticut and the greater northeast. JAX-GM has amassed a diverse array of technologies, computing capabilities and core research and support services to facilitate genomic research. Our M.D./Ph.D.-level scientists have specialties in cancer, diabetes, immunology, stem cell biology, computational biology, genomic technologies, the human microbiome, and infectious disease.