If you are affiliated with The Fred Hutch / University of Washington Seattle Cancer Consortium we would love to hear from you.
In 2020 our teams focus areas will include scRNA-Seq, ATAC-Seq, CyTOF, and other technologies relevant to immunology.
Multiomic Analysis of COVID-19 Patients
COVID-19 Progression Following Diagnosis
To interrogate how the immune system is altered from uninfected to mild to moderate to severe disease, we have characterized the circulating immune cell classes and collected plasma multi-omic profiles, on a cohort of 139 COVID-19 patients (265 samples) within the context of clinical observations, and compared them against 268 healthy donor samples. This view is built by first extracting the demographics and static and dynamic clinical features for each patient from their electronic health record (EHR). This clinical picture is then integrated with multi-omic analysis of two sequential blood draws per patient, the first of which was collected shortly after initial clinical diagnosis (time = T1), and the second a few days later (T2). For each blood draw, the plasma levels of around 500 proteins and 1000 metabolites were quantified along with single-cell multi-omic analyses of PBMCs in which whole transcriptome, 192 surface proteins, 32 secreted proteins, and T cell and B cell receptor gene sequences were measured.
Joint Representation Of Single-Cell Multi-Omics Data
Single-Cell Multi-Omics Data
Here, we present a joint clustering algorithm in Seurat, a general and interpretable framework for integrating single-cell multi-omics data. Unlike to other existing methods, Seurat calculates cell-specific modality importance by within and cross-modality prediction. Then, using the cell-specific modality importance, Seurat builds a joint nearest neighbor graph, which minimizes the weighted modality reconstruction error, to represent information from multiple modalities. The joint nearest neighbor graph can be used for all downstream analyses, such as clustering, visualization, trajectories.
Atlas of Gene Expression During Development
A Human Cell Atlas Of Gene Expression During Development
The emergence and differentiation of cell types during human development is of fundamental interest. We applied an assay for single cell profiling of gene expression based on three-level combinatorial indexing (sci-RNA-seq3) to 121 fetal tissues representing 15 organs, altogether profiling transcription in 4-5 million single cells. From these data, we identify and characterize diverse human cell types and annotate them with respect to marker genes, expression and regulatory modules. We focus our initial analyses of these data on cell types spanning multiple organ systems, e.g. epithelial, endothelial and blood cells, as well as on relating these data to a mouse atlas of organogenesis.
Atlas Of Chromatin Accessibility During Development
A Human Atlas Of Chromatin Accessibility During Development
To generate a human cell atlas of chromatin accessibility using tissues obtained during development, we devised an improved assay for single cell profiling of chromatin accessibility based on three-level combinatorial indexing (sci-ATAC-seq3). We applied this method to 53 fetal tissue samples representing 15 organs, altogether profiling on the order of one million single cells. We leveraged cell types defined by gene expression in the same organs to annotate these data, and then built a catalog of hundreds-of-thousands of candidate gene regulatory elements exhibiting cell type-specific accessibility. This resource allows, for example, the identification of lineage-specific transcription factors, prediction of cis-regulatory interactions based on co-accessibility or calculation of cell type-specific enrichments of complex trait heritability.
Mouse Organogenesis Cell Atlas
A single-cell transcriptional landscape of mammalian organogenesis
Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Over the course of a month we developed a series of visualizations to showcase ~2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation. Users can explore hundreds of cell types, 56 trajectories and thousands of corresponding marker genes expression over time. We are currently engaged with Brotman Baty to develop a scalable solution for sharing data generated from single-cell experiments.
Unlock the potential of linked clinical and molecular data
To overcome the persistent challenges in cohort discovery and analysis for translational research and prospective clinical-decision support in precision medicine, we developed Oncoscape. This interactive tool provides an online open-access data-analysis and visualization platform that empowers researchers and clinicians to discover novel patterns and relationships between linked clinical and molecular data. Oncoscape offers a unique and intuitive approach to iterative hypothesis refinement and cohort discovery by establishing a platform that allows users to traverse data types and methods easily. Visioned by Dr. Eric Holland, we remain committed to the growth of the Oncoscape platform and support the Seattle Translational Tumor Research group.
HICOR IQ combines Washington State cancer registry and health insurance data to offer reporting on quality of care and cost in oncology.
Principle members of the Fred Hutch Data Visualization team were recruited to reimagine the HICOR-IQ platform. In a little over the month we designed, developed and deployed a new site. HicorIQ mantained by the Hutch Data Commonwealth.