Title: Research Scientist, Genomic Data Science Core Department: Biomedical Data Science FLSA: Exempt Reports to: Director, Genomic Data Science Core Date: June 2025
Position Summary
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. Singularity 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).
Researchers at Dartmouth encompass 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.
Primary Responsibilities
NOTE: This position is dependent on the availability of sponsored funding
Contribute to experimental design and development of appropriate data analysis plans.
Perform genomic data analysis for core users.
Establish and maintain an open line of communication with users working to deadlines and specifications outlined in the analysis plan.
Provide clear and detailed analysis summary reports to core users.
Develop and maintain genomic data analysis pipelines and workflows.
Use RADAR for analysis time and fee tracking.
Work in a collaboratively and participate in team meetings as well as meetings with core users.
Provide scientific training and mentoring to other core members or core users where required.
Assist in the preparation of publications and grant proposals.
Become familiar with all services offered and serve as a point of contact for users.
Present at local, regional, and national meetings to expand the visibility of the core.
Engage in professional development opportunities such as workshops and courses to acquire new skills.
Performs other duties as required.
Minimum Qualifications
Proficiency with BASH and R or Python
Proven organizational skills with exceptional attention to detail.
Ability to work independently, taking initiative to advance projects.
Demonstrated experience analyzing next generation sequencing data.
Ability to use sound judgment and communicate effectively with users and facility staff.
Ability to effectively collaborate and interact with all levels of staff and management.
At least five years of relevant experience.
Requires a minimum of a BS degree with experience in genomics, bioinformatics, data science, or a related field.
Preferred Qualifications
Experience working in a core facility or other customer-facing or service-oriented role.
Experience analyzing single cell and/or spatial genomic data.
Experience optimizing computational workflows to increase efficiency and improve turnaround times
Basic understanding of molecular biology, immunology, microbiology or cancer biology
Attendees at the 2025 CQB Annual Meeting listen to Dr. Li Song’s Presentation
On April 8th, members of the CQB community gathered at the Hanover Inn to recognize our Center through a dynamic program featuring project leader presentations, updates on core services and usage, and a poster session where trainees shared their work.
CQB Director and PI, Michael Whitfield, PhD delivers Opening Remarks
Michael Whitfield, PhD
CQB Director, Dr. Michael Whitfield, started off the meeting with a brief overview of the center’s progress and successes from Phase 1 which included; leveraging COBRE funding by more than 3:1, receiving substantial supplements, graduating four project leaders who secured significant funding, successfully onboarding new research and pilot project leaders, and supporting eight faculty hires. Dr. Whitfield highlighted a strong start to Phase 2, including receiving additional supplements, graduating two project leaders, onboarding a new project leader, hiring a new research scientist for the GDSC, and hosting a symposium on single cell and spatial transcriptomics.
Project Leader Li Song, PhD presents “Predicting TCR and BCR specificity to microbiomes by massively mining RNA-seq samples”
Li Song, PhD
Dr. Li Song’s project focuses on predicting T-cell receptor (TCR) and B-cell receptor (BCR) specificity to microbiomes by extensively analyzing publicly available RNA-seq data. The motivation stems from the current limitations in discovering antigen-specific TCRs and BCRs, which are often low-throughput and hindered by the limited annotations in existing specificity databases. The core rationale is that valuable data on immune repertoires (TCR/BCR), microbiome composition, and related sample information can all be extracted from RNA-seq samples found in large public repositories. To facilitate this, his lab has developed and refined computational tools: TRUST4 for efficient immune repertoire reconstruction from bulk and single-cell RNA-seq data, and T1K for accurate and versatile genotyping of the highly polymorphic MHC (HLA) genes from sequencing data. These tools allow for the recovery of personalized immune-related information from RNA-seq.
In parallel with immune repertoire analysis, the research addresses the challenge of characterizing the microbiome from the same RNA-seq data. Traditional taxonomic classification relies on searching microbial genome databases. Dr. Song’s lab has developed Centrifuger, a software tool that utilizes lossless compression of microbial genomes for highly efficient and accurate taxonomic classification. Centrifuger demonstrates improved accuracy across taxonomy ranks and is computationally efficient compared to other methods. It can also be applied effectively to single-cell RNA-seq data. A major future direction for Dr. Song is to connect the immune repertoire information (TCR/BCR) with the microbiome data derived from this massive collection of RNA-seq samples.
Project Leader Jennifer Hong, MD presents “Understanding brain extracellular matrix in the tumor microenvironment”
Jennifer Hong, MD
Dr. Jennifer Hong investigates the molecular mechanisms underlying brain tumor-related epilepsy (BTRE), a significant complication affecting a high percentage of brain tumor patients with unexplained variability across tumor subtypes and limited effective therapies. BTRE is highlighted as the most critical risk factor for long-term disability in these patients, impacting their independence. The project posits a central hypothesis that the loss of perineuronal nets (PNNs), specialized extracellular matrix components found in the brain tumor microenvironment, drives the onset of seizures. PNNs, composed of neuronal and glial-secreted components, can bind various molecules including neurotransmitters and ion channels and are susceptible to degradation by enzymes like MMPs and ADAMTS. Understanding the role of PNNs and the tumor microenvironment may offer novel targets for seizure suppression.
To explore this hypothesis, Dr. Hong employs a multi-modal approach utilizing a neurosurgical biorepository of ex vivo specimens with associated clinical data. A key method is spatial transcriptomic analysis using Digital Spatial Profiling, which allows assessment of PNN components and morphology within defined regions of interest (ROIs) across different spatial compartments (Brain, Intermediate, Tumor). Analysis has identified differentially expressed genes based on spatial compartment and seizure status. Dr. Hong is developing a machine learning-based multi-modal model integrating transcriptomic, morphology/histology, and clinical data for biomarker discovery and outcome prediction. Initial testing of this model using data from a limited patient cohort demonstrated an 82% accuracy in predicting seizure outcome.
Audience members listen attentively
Project Leader Lauren Walker, PhD presents “Neuronal guidance landscape across development and regeneration”
Lauren Walker, PhD
The research presented by Dr. Lauren Walker investigates the molecular mechanisms that guide motor neurons to their correct targets during development and, importantly, during regeneration after injury. This project addresses a critical biological question with high clinical relevance, given the impact of peripheral nerve injuries and neuropathies on millions of Americans. Dr. Walker utilizes the larval zebrafish pectoral fin as a model system, which is advantageous due to its optical transparency, significant genetic conservation with humans (70-80% of disease genes), and its well-defined, stereotyped innervation pattern. In this model, motor axons navigate multiple distinct “choicepoints” to reach the correct muscle and specific domain within that muscle. Regeneration in this system is notably rapid, robust, and functional, with a high degree of accuracy in motor neurons returning to their original targets (97% to muscle, 85% to domain), making it an ideal system to dissect the underlying guidance mechanisms.
Dr. Walker’s project aims to determine the “molecular code” that directs this precise axonal targeting by identifying the guidance receptors present on distinct motor neuron populations and mapping the spatial organization of corresponding guidance cues in the surrounding tissue. The research is structured around two main aims: first, determining the molecular signature that defines different motor neuron subpopulations and second, defining the spatial organization of guidance cues during both development and regeneration.
Genomic Data Sciences Core Co-Directors Shannon Soucy, PhD and Owen Wilkins, PhD share core updates
Owen Wilkins, Co-director GDSC
The Genomic Data Science Core (GDSC), co-directed by Drs. Shannon Soucy and Owen Wilkins, serves Dartmouth researchers by providing various services related to genomic data analysis. The core focuses on several key areas: infrastructure activities such as pipeline generation, maintenance, and optimization; hosting genomic reference files and software environments; exploring the feasibility of cloud compute platforms for storage and analysis; and implementing system-wide storage solutions like Starfish. They also conduct training activities, planning workshop redesigns, and offer analysis activities covering a wide range of genomic data types including metagenomics/meta-transcriptomics and spatial transcriptomics. The core has seen a continued trend of high utilization, with a notable increase in metagenomic/meta-transcriptomic projects. Significant time has also been invested in developing analysis workflows for spatial transcriptomics technologies like Xenium and Visium HD.
Single Cell Genomics Core Director Fred Kolling, PhD shares core updates
Fred Kolling, PhD
The Single Cell Genomics Core (SCGC), directed by Fred Kolling, PhD, is a core facility that supports research through single cell and spatial omics. As part of the larger Dartmouth Shared Resource ecosystem, the SCGC provides flexible sample processing for various sample types, including tissues, FFPE, blood, and cells. This “any sample, any analyte” approach encompasses processes like sample preservation, processing, QC, counting, tissue dissociation, and nuclei isolation. The core supports translational science, partly through sample preservation methods like 10x Genomics Flex, which allows fixing and storing samples at collection or isolating nuclei from frozen samples or FFPE blocks. Nuclei isolation is a critical step, particularly for certain applications or non-human/mouse samples, and the SCGC emphasizes optimizing nuclei quality, recognizing its direct impact on data quality. The core serves both CQB members and an expanding non-CQB user base, including various external academic and institutional partners.
The SCGC benefits from integration with other Dartmouth resources, such as the Pathology Shared Resource for tissue processing and staining and collaborates with the Genomic Data Science Core (GDSC) which builds analysis frameworks for data types like Xenium. The SCGC offers spatial transcriptomics capabilities via Visium and Xenium technologies and have completed nine Xenium projects.
Patricia Pioli, PhD, Yina Huang, PhD, Fred Kolling, PhD present their Team Science Project “Attacking Fibrosis with Armored CAR T Cells”
This Team Science project focuses on developing a novel therapeutic strategy to attack fibrosis, particularly in the context of Systemic Sclerosis (SSc), described as a deadly fibrotic disease with no known cure. SSc is a progressive, chronic multi-system disorder leading to dermal and internal organ fibrosis, loss of subcutaneous fat, vasculopathy, and inflammation, disproportionately affecting women in their 5th-6th decade of life. The loss of subcutaneous fat is noted to precede the development of fibrosis in SSc. The central hypothesis of this work is that targeting CD206+ macrophages will reduce fibrosis. These CD206high macrophages are thought to induce SSc fibroblast activation through factors like TGF-β, IL-6, and CCL2. Given the lack of therapeutics that specifically target macrophages, the researchers propose using Anti-CD206 Chimeric Antigen Receptor (CAR) T cells to eliminate these key pro-fibrotic cells, aiming for durable response and potential to secrete anti-fibrotics, drawing on the success of CAR T cells in treating other autoimmune diseases.
To investigate this, the team is utilizing techniques such as scRNA-seq and spatial transcriptomics (Visium HD) on mouse skin models of fibrosis to identify cell types, phenotypes, and assess if anti-CD206 CAR T treatment reverses changes associated with fibrosis. Challenges with Visium HD, including the per-pixel output, necessitate generating a single-cell atlas for reference-based annotation of cell types like adipocytes, monocytes, and fibroblasts. Initial findings and refined experiments, including using improved slides for tissue adhesion and transcript fidelity, suggest that elimination of CD206 high macrophages significantly attenuates early but not established fibrosis.
Sladjana Skopelja-Gardner, PhD and Lucas Salas Diaz, PhD present their Team Science project “Defining the pathogenic role of myeloid cell populations in glomerulonephritis using Spatial Transcriptomics and DNA Methylation”
Sladjana Skopelja-Gardner, PhD
Lupus Nephritis (LN) is a significant complication of Systemic Lupus Erythematosus (SLE), identified as a major risk factor for morbidity and mortality; patients with LN face a higher standardized mortality ratio and die earlier than SLE patients without LN, with 10% eventually developing End-Stage Renal Disease (ESRD). Although neutrophils are known to infiltrate the kidneys in LN, their specific pathogenic role is not well understood. To address this knowledge gap, a collaborative project involving the Skopelja-Gardner Lab, Salas Lab, and Genomic Data Science Core is defining the pathogenic role of myeloid cell populations in LN using advanced techniques like Spatial Transcriptomics and DNA Methylation. This research integrates spatial transcriptomics, scRNAseq, and urine DNA methylation to investigate the location and impact of intra-renal neutrophils, their heterogeneity (including normal-density, low-density, and CXCR4+ populations), and the potential for non-invasive quantification. The project specifically aims to determine if spatial transcriptomics can study neutrophils in LN kidneys, define their heterogeneity, and quantify them non-invasively to predict disease course. The project utilizes urine DNA methylation as a method to non-invasively quantify neutrophils in relation to clinical parameters.
Patricia Pioli, PhD presents her Women’s Health Supplement “Estrogen Regulation of Macrophage Activation in Systemic Sclerosis”
Patricia Pioli, PhD
Dr. Patricia Pioli investigates the role of estrogen in regulating macrophage activation in Systemic Sclerosis (SSc), a severe fibrotic disease with no cure. SSc disproportionately affects women, with a diagnosis rate of 5:1 compared to men, increasing to 10:1 during child-bearing years. The average age of onset in women (43.6 years) coincides with changes in sex hormone levels, and increased levels of estrone (E1) and estradiol (E2) are observed in post-menopausal women and older men with SSc, correlating with greater disease severity. Furthermore, menopausal hormone therapy and hormonal therapy initiation in transgender individuals have been linked to an increased risk of SSc. Mounting evidence suggests a potential role for sex hormones in SSc pathogenesis, and this research aims to understand precisely how estrogens influence SSc macrophage activation and how these activated macrophages contribute to fibrosis.
To explore these mechanisms, Dr. Pioli involves several approaches, including evaluating how estrogens modulate the pro-fibrotic immunophenotype of SSc macrophages. Current goals include focusing on how estrogen-activated SSc macrophages contribute to fibrosis by evaluating collagen deposition and tissue thickness using H&E staining. A key technique being employed is single-cell RNA sequencing (scRNA-seq) of 3D tissue macrophages to determine how estrogens modulate the heterogeneity of macrophage subsets.
Duncan Morhardt, MD/PhD presents his pilot project “Spatial transcriptomic dissection of the vertebrate urinary development program using zebrafish”
Duncan Morhardt, MD, PhD
The Morhardt’s Lab is conducting research focused on mapping the emerging function and associated transcriptomic changes in the developing zebrafish bladder. This project utilizes the zebrafish as a model organism, leveraging its advantages such as abundant genetic tools and transparent development. The project aims to understand how the zebrafish bladder develops, addressing questions relevant to pediatric bladder diseases like Exstrophy Spina Bifida, Cancer, and Agenesis. Dr. Morhardt uses imaging techniques, including pericardial injection, to image the bladder and measure bladder function by tracking fluorescent intensity changes over time in the bladder and urine stream. He is also performing molecular characterization of the developing bladder.
Transcriptomic analysis using Bulk RNA Seq of pooled specimens reveals age-dependent clustering of genes. Data was presented on the expression levels of various receptor genes, including Muscarinic, Adrenergic, and Purinergic receptors, across different developmental time points. The project seeks to use the numerical advantage of zebrafish to improve the detection of pathologic developmental issues in the genitourinary system, potentially after chemical exposure.
Project Leader Britt Goods, PhD presents “Mapping the impact of sex hormones on macrophage fates and functions”
Britt Goods, PhD
Dr. Britt Goods focuses on mapping the impact of sex hormones on macrophage fates and functions. Macrophages are highlighted as key mediators of health and disease and are known to be highly heterogeneous. Her project emphasizes the need for a standardized approach to explore how sex hormones influence these diverse immune cells. Dr. Goods utilizes a well-established ex vivo system employing multi-omic methods to study macrophages.
The experimental approach involves isolating monocytes from PBMCs and differentiating them into M0, M1, and M2 macrophage subtypes. These differentiated cells are then exposed to varying concentrations of 17β-estradiol (E2) or progesterone (P4) over different time points. The impact is assessed through bulk RNA-Sequencing and analysis of cytokine secretion via Luminex. Single-cell RNA sequencing (snRNA-seq) is also used to profile transcriptomes without requiring cell detachment. Dr. Goods plans to extend her findings in vivo by studying macrophages in tissues like the cervix and vaginal tract using non-invasive sampling.
Anne Hoen, PhD, Benjamin Ross, PhD, Li Song, PhD, Mark Sundrud, PhD present their Team Science Project “Multi-omics modeling of bacterial metabolism and effects on immune physiology”
Anne Hoen, PhD, Benjamin Ross, PhD, Li Song, PhD, Mark Sundrud, PhD
The team presented on the multi-omic interrogation of host-microbe interactions mediated by metabolites produced by gut bacteria, specifically investigating their role in immune regulation. The project explores metabolites like tryptophan metabolites and bile acids. To address key questions driving the research, a multi-omic approach leverages mice with different microbial colonization statuses: Germfree, Conventional, and SGM C57BL/6J mice, utilizing techniques such as LC-MS and scRNA-seq.
The teams analytic workflow involves exploring covariate relationships and standardizing ‘omic data, analyzing correlations between standardized ‘omic data, building networks using a Gaussian graphical model, and performing network analysis including enrichment and metabolite-specific subnetwork analysis. Using scRNA-seq, the researchers are pursuing metabolite-gene interactions, identifying clear clusters of B, T, and innate lymphoid cells in samples from germfree, SGM, and conventional mice.
The Event Concludes with a Reception and Poster Session in the Grand Ballroom
CQB Project Leader Lauren Walker, PhD. has been named the Frank and Myra Weiser Scholar in the Neurosciences at the Geisel School of Medicine! Learn more in this Geisel News Article!
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.
Primary Responsibilities
NOTE: This position is dependent on the availability of sponsored funding
▪ Contribute to experimental design and development of appropriate data analysis plans.
▪ Perform genomic data analysis for core users.
▪ Establish and maintain an open line of communication with users working deadlines and specifications outlined in the analysis plan.
▪ Provide clear and detailed analysis summary reports to core users.
▪ Develop and maintain genomic data analysis pipelines and workflows.
▪ Use RADAR for analysis time and fee tracking.
▪ Work collaboratively and participate in team meetings as well as meetings with core users.
▪ Provide scientific training and mentoring to other core members or core users where required.
▪ Assist in the preperation of publications and grant proposals.
▪ Become familiar with all services offered and serve as a point of contact for users.
▪ Present at local, regional, and national meetings to expand the visibility of the core.
▪ Engage in professional development opportunities such as workshops and coureses to acquire new skills.
Anticipated salary will be between $70,000 and $85,000 depending on candidate qualifications and work history.
Qualifications
Minimum Qualifications
▪ Proficiency with BASH and R or Python.
▪ Proven organizational skills with exceptional attention to detail.
▪ Ability to work independently, taking initiative to advance projects.
▪ Demonstrated experience analyzing next generation sequencing data.
▪ Ability to use sound judgement and communicate effectively with users and facility staff.
▪ Ability to effectively collaborate and interact with all levels of staff and management.
▪ At least five years of relevant experience.
Preferred Qualifications
▪ Experience working in a core facility or other customer-facing or service-oriented role.
▪ Experience analyzing single cell and/or spatial genomic data.
▪ Experience optimizing computational workflows to increseefficiency and improve turnaround times.
▪ Basic understanding of molecular biology, immunilogy, microbiology or cancer biology.
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.
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 Resourcesfor 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
Attendees between presentations
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
Introduction and Updates
Director, Dr. Michael Whitfield, presents a Center overview
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.
Project Summaries
Attendees listen to Dr. Li Song present his project updates
“Predicting TCR and BCR specificity to microbiomes by massively mining RNA-seq samples” Project Lead: Li Song
Project Leader, Dr. Li Song presenting
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.
“Mapping the impact of sex hormones on macrophage fates and functions” Project Lead: Britt Goods
Project Leader, Dr. Britt Goods (left), talks with students at the post-meeting poster session
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.
“Gene regulation and dynamical efficacy in antibiotic responses” Project Leader: Daniel Schultz
Project Leader, Dr. Daniel Schultz in conversation
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.
“Computational approaches to studying somatic mutations in cancer” Project Leader: Siming Zhao
Project Leader, Dr. Siming Zhao
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:
Developing new methods and analysis to map genetic variants with context dependent regulatory effect
Identifying the cellular context for disease-causing genetic variants
Inference of disease relevance using single cell omics data
“Understanding brain extracellular matrix in the tumor microenvironment” Project Lead: Jennifer Hong
Project leader Dr. Jennifer Hong in conversation
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.
“Revealing the impact of tryptophan metabolism on host-microbe and microbe-microbe interactions in the gut” Pilot Project Leader: Ben Ross
Project Leader, Dr. Ben Ross
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.
“Spatial transcriptomic dissection of the vertebrate urinary development program using zebrafish” Pilot Project Leader: Duncan Morhardt
Dr. Duncan Morhardt (right) in conversation
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.
Team Science – “Multi-omics modeling of bacterial metabolism and effects on immune physiology” Leads: Anne Hoen, Benjamin Ross, Li Song, andMark Sundrud
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:
What is the size and complexity of intestinal bile acid and tryptophan metabolite pools?
How and where are they absorbed to interface with host immune and epithelial cells?
How do they influence host gene expression?
What are the similarities and differences between these pools in mice with intact vs. minimal microbiomes?
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.
Team Science – “Defining the pathogenic role of myeloid cell populations in glomerulonephritis using Spatial Transcriptomics and DNA Methylation” Leads: Sladjana Skopelja-Gardner and Lucas Salas Diaz
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.
Single Cell Genomics Core Core Director: Fred Kolling
Members of the audience at Dr. Fred Kolling’s presentation
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.
Poster Session
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.
One of many animated conversations at the poster session
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.
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.
