Our CQB hosted Spatial Transcriptomics Symposium had over 100 attendees and was featured in the Geisel Insider! You can read the article at https://geiselmed.dartmouth.edu/news/2024/geisel-hosts-first-single-cell-and-spatial-transcriptomics-symposium-at-dartmouth/ and access recordings of a selection of speakers at https://sites.dartmouth.edu/cqb/symposium-videos/.
Our very own Britt Goods was quoted in the The Scientist! Read the article here: “Viral Activation Can Shape Breast Milk Composition“.
UPDATE: This position has now been filled!
Position URL: apply.interfolio.com/148940
The Genomic Data Science Core (GDSC), part of the Center for Quantitative Biology at Dartmouth, is seeking a Research Scientist to analyze next generation sequencing data and provide biological insight to the results. The GDSC is a fee-for-service bioinformatics core facility aimed at providing computational expertise to the Dartmouth faculty in advancing their research goals. The Research Scientist will work as part of the GDSC team to analyze high-dimensional genomic datasets. The ability to work effectively as part of a team is essential to be successful in this position.
The ideal candidate is expected to have knowledge and/or experience analyzing large datasets from multiple major genomic modalities such as RNA-seq, ATAC-seq, WES, WGS, ChIP-seq, and metagenomics. Experience with other types of genomic data (e.g. methylation/SNP arrays, TCR-seq, Nanostring) is a plus, and experience with single-cell or spatial transcriptomics is especially desirable. The candidate should have familiarity with computational tools used for these analyses and the ability to write scripts in BASH, R, and/or Python. Furthermore, the candidate should have a working knowledge of basic statistical concepts (regression analysis, linear modeling, probability). Experience with any of the following are also highly desirable: use of workflow management software (e.g. Snakemake, Nextflow) to construct and maintain data analysis pipelines, software management with package managers (e.g. Conda) or containers (e.g. Singualrity or Docker), version control with Git/GitHub, familiarity with public genomics databases (GEO, SRA, NCBI, Ensembl), experience with genome browsers (e.g. IGV), high performance computing systems and/or cloud computing environments (GCP, AWS).
The core supports researchers at Dartmouth from a broad range of biological disciplines, including cancer biology, immunology, neurology, microbiology, and environmental science. As such, the candidate must have a desire to work in a highly collaborative research environment and possess strong communication and organizational skills to manage and multitask diverse analysis projects. The Research Scientist will be responsible for tracking his/her time committed to each project for billing purposes. Due to the service-based nature of this role, being able to work to established deadlines is a crucial requirement. Presenting at conferences and seminars is encouraged, and opportunities for career development such as workshops and coursework will be supported.
NOTE: This position is dependent on the availability of sponsored funding
Anticipated salary will be between $70,000 and $85,000 depending on candidate qualifications and work history.
Application Instructions
Please apply via Interfolio using this link.
Dartmouth College is an equal opportunity/affirmative action employer with a strong commitment to diversity and inclusion. We prohibit discrimination on the basis of sex, race, color, religion, age, disability, status as a veteran, national or ethnic origin, sexual orientation, gender identity, gender expression, or any other category protected by applicable law, in the administration of its educational policies, admission policies, scholarship and loan programs, employment, or other school administered programs. Applications by members of all underrepresented groups are encouraged.
If you are an applicant with a disability and need accommodations to assist in the job application or interview process, please email ADA@dartmouth.edu. In the subject line, please state “Application Accommodations” and include the job number or title. Someone from the ADA Compliance Office will be in touch within 2 business days.
For additional employment opportunities at Dartmouth College, please visit the Dartmouth Interfolio Job Board, the Office of the Provost, and the Office of Human Resources.
Offers of employment are contingent upon consent to a pre-employment background check with results acceptable under Dartmouth policy. Please visit the Office of Human Resources for details.
All Dartmouth College employees must comply with the College’s health and safety guidelines and protocols, including but not limited to those related to COVID-19, such as any testing, masking, or distancing requirements that may be in place at any given time or place.
Photos: Lars Blackmore
Text: Tammara Wood and Karin Curtis-Hill
On April 3rd, 2024 members of the Center for Quantitative Biology (CQB) gathered at the Hanover Inn to share their updates, accomplishments, and plans, with the CQB community, meet with their External Advisory Committee (EAC), and enjoy some good company and good food!
EAC Members:
Dr. Kelley Thomas, University of New Hampshire
Dr. Cathy Wu, University of Delaware
Dr. Paul Robson, Jackson Laboratory
The meeting kicked off with a brief center overview from our director, Dr. Michael Whitfield, highlighting the successes from Phase 1.
He discussed how Dr. Zhao is awaiting her NOA due July 2024, opening a slot for a new project leader in Phase 2. Also highlighted was our new research project lead, Dr. Jennifer Hong, the recruitment of two new faculty members; Drs. Ken Hoehn and Lauren Walker, and ongoing searches in the Department of Biomedical Data Science, the Center for Technology and Behavioral Health, and the Center for Precision Health and Artificial Intelligence.
Both Single Cell Genomics and Genomic Data Science Cores have been extremely busy with investments in instruments and infrastructure, providing pilot grants for new users and/or novel projects. More than $2.5M in COBRE Supplements were funded in Year 5, including Team Science and Cloud Computing awards. A Women’s Health Supplement will be funded soon.
Dr. Li Song presented a brief overview of his background before discussing why T Cell Receptors (TCRs) and B Cell Receptors (BCRs) are important. He explained how researchers have developed many different experimental approaches to discover the antigen-specific TCRs or BCRs and how these experimental platforms are usually low-throughput and the computational approach can only inspect a limited number of receptors, antigens, or species, usually with little information for BCRs. His lab has developed computational methods to extract both biologically meaningful microbiome and immune repertoire information from RNA-seq data.
His group applied their TRUST4 algorithm to TCGA and cancer immunotherapy samples to study the immune repertoire in the tumor microenvironment. Although methods like TRUST4 and T1K can infer the immune-related information from the data, including TCR, BCR and HLA genotypes for MHC, they still do not know the origin of these antigens, including those from microbes. To get at this information his group plans to map the reads to the microbial genome database to identify taxonomy information for each read, but memory usage was a major concern. A common approach to combat this is to use memory-efficient data structures, such as those adopted in the method Centrifuge; however, this too is doomed to be a bottleneck of the memory usage in data analysis as the database grows. Dr. Song’s solution was to develop a lossless compression of microbial genomes for efficient taxonomic classification called Centrifuger, a paradigm similar to the method Centrifuge but with significantly lower memory requirement and higher sensitivity and precision.
Dr. Song’s future work will focus on immune receptor analysis, extending it to more sequencing platforms; microbiome analysis to improve accuracy, pangenome, translated search, adaptive sampling, and quantification; and to connect the immune repertoire and microbiome information from a large number of RNA-seq data.
Dr. Britt Goods’ work addresses unmet needs in reproductive health and immunology by applying and developing systems biology tools across biological scales. Her lab aims to solve problems surrounding macrophage fates and functions, non-hormonal contraceptives and identifying drivers of health and disease. Her presentation focused on macrophages and the impact of sex hormones impacting their function. Her data showed, after exposure to estrogen, a pronounced alteration of the global transcriptome for differentiated macrophages with M1-like pro-inflammatory macrophages both time and dose dependent and M2-like anti-inflammatory macrophages only time dependent. She also showed how treatment with progesterone impacts M1-like macrophages that are distinct from those of estrogen.
In conclusion, Dr. Goods showed that her group can generate polarized inflammatory and anti-inflammatory macrophages. They also found that estrogen and progesterone exposure alters the global transcriptome of human macrophages with M1 macs impacted more than M2 macs. She showed how genes and pathways related to immune responses were impacted and provided data on cytokine measurements that suggest E2 increases secretion of VEGF and CCL2. Her future work will focus on understanding the impact of estrogen and progesterone on macrophage differentiation and expand to testosterone and luteinizing hormone.
Dr. Daniel Schultz’ lab is interested in the dynamics of cell processes, with a focus on antibiotic responses in bacteria. His lab combines experimental and computational approaches using microfluidics, experimental evolution, mathematical modeling and bioinformatics. He provided an overview of how antibiotic responses evolve to adapt to new complex environments, such as in clinical settings. His lab developed a specialized microfluidic device to study structured biofilm-like microcolonies instead of just single cells, that more traditional microfluidic devices track.
He showed how regulation of the E. coli tetracycline resistance tet operon can be lost when evolved in fast-changing drug regimens. He presented data on how induction of the wild type mexXYZ multidrug resistance mechanism in P. aeruginosa is slow in natural environments, but once in the human lung it acquires a similar loss of regulation to speed up the response. He also showed how understanding the effect of regulatory pathways in the resistance phenotypes of microbes allows treatment planning that addresses specific drug-resistance profiles.
Dr. Siming Zhao began her presentation with the central question in human genetics, ‘What are the disease-causing variants and how do they cause disease?’ She explained how GWAS, although a powerful computational approach, has significant challenges. Her group’s solution is to integrate GWAS with functional genomics data. Her recent publication, Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits, in January 2024, describes how her lab’s new method, causal TWAS (cTWAS), provides a robust statistical framework for gene discovery.
Moving forward, Dr. Zhao has three areas of proposed work:
Dr. Jennifer Hong began by presenting images of brains with tumors, asking the group which patient had a seizure. Seizures are often the first presentation of brain tumors, with the reappearance or worsening of seizures indicative of tumor progression, yet there are striking differences in the rate of seizures in these patients that are unexplained. Seizures are considered the most important risk factor for long- term disability in brain tumor patients, impacting both mental and physical fitness and thus independence. Her work focuses on the unmet clinical need by understanding the tole of the tumor microenvironment in initiating seizures in patients with brain tumors.
Dr. Ben Ross provided an update on his project where his group has successfully established SGM consortium in gnototiobic mice, successfully detected and quantified tryptophan metabolites, how sex has a significant impact on SGM composition, and how possibly his group has not successfully eliminated indole production.
Dr. Ross plans to finish analysis of banked samples, initiate SGM 2.0 experiments, and prepare a manuscript of his work.
Dr. Duncan Morhardt discussed the pros and cons of working with zebrafish as a means of understanding the conserved, critical mechanisms of external urethral development. His project proposes to catalogue the expression landscape of urethral development in zebrafish using RNA-seq to inform probes in the Xenium platform. He provided his group’s initial anatomic studies of zebrafish urinary structures and urinary morphology over time. He explained how his lab is making headway on transcriptomic characterization, optimizing experimental conditions, and leveraging (quantitative) advantages of the zebrafish. His next step is use of the Xenium platform and analysis of early and late bladder development of zebrafish.
The team presented their current work on using novel multi-omics approaches to investigate the form and function of key microbiota-derived metabolites on host gene expression in the gut. They began with their key questions:
They then walked the group through their new approach for tracking the production and metabolism of microbe-derived gut metabolites in vivo, and in turn, how this information can be used to infer rates and routes of intestinal absorption. They discussed combining their multi-omics approaches with gnotobiotic methods to interogate microbial biosynthetic mechanisms underlying the production of discrete intestinal metabolite species. They ended their presentation with a variety of computational workflows that have been developed for meta-transcriptomics analysis, as well as differential and correlation network analyses.
Dr. Skopelja-Gardner discussed the role of neutrophils in lupus nephritis (LN) pathogenesis and how myeloid cells mediate glomerulonephritis. Their group was able to confirm neutrophil presence in the glomeruli using spatial transcriptomic analyses of LN tissues. They determined Low Density Neutrophils and High Density Neutrophils infiltrate LN kidneys and that CITEseq reveals distinct neutrophil gene signatures in healthy vs. lupus. They confirmed neutrophils are elevated in the urine of LN patients with active disease and that urine DNA methylation deconvolution analysis can be used to non-invasively quantify neutrophils in relation to clinical parameters.
Dr. Shannon Soucy provided an overview of the progress made by the Genomic Data Science Core over the last year. She presented the group with core usage numbers broken down by service categories that included epigenetics, gene expression, single cell and spatial. She then presented a graph on GDSC Training Workshop attendance, providing a of breakdown of users by CQB affiliation. Dr. Soucy also provided an update on GDSC Interns and their work, mentioning the core is now accepting 2024 internship applications from the QBS MS program. GDSC infrastructure includes analysis portfolio pipelines/workflows, large scale parallelization and genomic references on DartFS/Discovery. This segued into the feasibility of leveraging GCP Cloud Computing, with Dr. Soucy discussing the Cloud Computing supplement, advantages/disadvantages and data storage optimization.
Dr. Fred Kolling provided an overview on how SCGC provides end-to-end workflow support for single cell and spatial transcriptomics studies, while integrating with GDSC for analysis. His talk focused on how SCGC will focus on the needs of the research community during Phase 2, with emphasis on expanding capabilities in Translational Research, Single Cell Multiomics, and Spatial Transcriptomics. The future directions of the core are to spread awareness of translational capabilities with clinical partners, continue to build relationships with other INBRE institutions to serve as a regional resource, validate post-Xenium workflows for IHC, multiplex-IF and other modalities – integration with microscopy shared resource, and incorporate Visium HD into spatial portfolio.
The day concluded with a poster session and reception with hors d’oeuvres and beverages. This was a chance for everyone to socialize and students to discuss their projects. We were joined by attendees of the AI Symposium next door, resulting in a large and lively crowd.
For anyone who wants to trial the soon-to-be-released 5K gene panels for Xenium, 10x has announced a grant program to fund an exciting project. The Single Cell Genomics Core and Genomics Shared Resource will support sample preparation, instrument run and data delivery should you receive the award.
Learn more and apply for the Grant
Continue reading “Grant Funding Available for a 2-slide run on the Xenium Prime”
A lot has been happening at the CQB and we are excited to share some of the most important news with you in our CQB Newsletter!
10x Genomics and the Center for Quantitative Biology teamed up for a grant program to support an innovative research project at Dartmouth. The winner received a Visium CytAssist slide, library prep and sequencing at no cost.
Optimization of data analysis ecosystems is an important frontier in data analytics science. Access to compute resources with sufficient RAM and technology to analyze genomic datasets is often a rate limiting step to deploying analysis workflows. Furthermore, as datasets become larger and more complex file sizes increase exponentially and storage costs increase in concert with the size of the data. The increased availability of compute resources and the multiple storage tiers available on the cloud has the potential to democratize complex data analytic algorithms that require state of the art technology to run analyses while reducing data storage costs by making use of archival storage for raw files.
The NIGMS has supported the creation of a series of self-guided data science training modules that leverage cloud compute resources to analyze a multitude of data types and analytic procedures. Each module is built around a jupyter notebook interface and will train users to conduct computational analyses of biological data using the Google Cloud Platform. These training materials are accessible through the NIGMS github site, but they will cost money to run using the Google Cloud Platform. The NIGMS and NH-INBRE are providing cloud credits to 10 people per cohort which will provide access to all NIGMS data science training modules for 6 weeks at a time. The dates are as follows:
Continue reading “Self-guided data science training modules”
photos: Lars Blackmore
On April 5th, 2023 the Center for Quantitative Biology held its annual meeting at the Hanover Inn in Hanover NH. The meeting is part of an annual review of the COBRE grant by the External Advisory Committee, but also an opportunity for members of the center and the larger community to get together, share their work, and make new acquaintances over breakfast or a glass of wine.
Continue reading “COBRE Center for Quantitative Biology Annual Meeting April 5, 2023”
In November, attendees of the Single Cell and Spatial Genomics Symposium began arriving at Auditorium G, DHMC, around 8:30am to grab some breakfast and caffeine before settling into their seats to learn about Single Cell Genomics and Spatial Genomics technologies.
Continue reading “Single Cell and Spatial Genomics Symposium”