Pacific Northwest National Laboratory Partnering Strengths
Pacific Northwest National Laboratory (PNNL) is a pioneer in technology development and application. Our scientists believe in strong, cross-functional collaborations through every stage of research.
Collaborating with PNNL means participating in a team-science environment with multi-disciplinary experts and creators of technologies best suited to tackle big research questions. This means working together from the initial study design to the final publication. Our open approach and shared passion for innovation allows us to tackle complex challenges in human health and to make a real-world difference, in partnership with OHSU.
Collaborating with PNNL through Pacific northwest bioMedical Innovation Co-laboratory (PMedIC)
is different from working with a research service or core. Scientists at PNNL are part of dynamic evolving teams that work deeply with collaborators to solve current challenges and to help develop plans to include omics, structural, functional, and computational capabilities.
Some PNNL capabilities are highlighted below. This is snapshot of our overall capability, and we are always inventing.
Please contact PMedIC or complete a Research Project Request form to discuss your research needs.
Starting work with Pacific northwest bioMedical Innovation Co-laboratory
When you agree on a project with your collaborator, you will be required to provide a Statement of Work and budget to cover costs of the collaboration. It is then necessary to complete a short contracting period before work can begin, including raising a purchase request in Oracle to cover these budget costs.
The PMedIC Leadership Team aims to facilitate this process as much as possible. You can always contact pmedicteam@ohsu.edu for assistance.
PNNL’s spatial ‘omics capabilities focus on advanced mass spectrometry technologies.
Spatial information is retained through either:
- Direct-capture from tissue* (e.g. N-glycans, extracellular matrix proteins, lipids)
- Laser capture microdissection* (deep spatial proteomics of up to 5,000 proteins)
*Sample preparation impacts the analyses possible
Collaborating researchers can generate datasets that map the distribution and abundance of proteins, metabolites, lipids, and glycans within a tissue, revealing functional organization and cellular interactions.
PNNL has developed the NanoPOTS (Nanodroplet Processing in One Pot for Trace Samples) and NanoSPLITS (Nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples) for proteome only, or proteome and transcriptome analysis from the same single cell.
These can be used to generate datasets that profile protein abundance and gene expression in individual cells, uncovering cellular heterogeneity and rare cell populations.
PNNL is a pioneer in multi-omics data generation and analysis. Genomics, transcriptomics, proteomics, protein post-translational modifications such as phosphorylation state, metabolomics, and lipidomics are combined to provide a holistic view of cellular processes and disease mechanisms.
PNNL researchers use statistical, machine learning, and network analysis of multi-omics data to enhance the understanding of biological systems. Their methods reveal complex interactions between different molecular layers and identify key regulatory hubs and pathways.
PNNL has deep experience in statistical analyses of large and disparate data sets. Their established data analysis platforms can integrate multiple data sets ranging from multi-omics analyses to clinical parameters. They continue to develop these platforms to find intuitive ways for scientists to interact with their data.
These approaches allow researchers to identify significant correlations and relationships within data, and to visualize their data in novel and useful ways, leading to new hypotheses and discoveries.
PNNL has a wide range of capabilities to understand protein structure, function, and interactions.
A selection of these technologies include:
- Cell-free protein synthesis
- CryoEM
- NMR characterization of proteins, protein dynamics, and small protein interactions
- Protein Integrity Solubility Alteration Assay (PISA) for drug target identification
- Protein computational modeling
Collaborating researchers may generate data that informs how proteins structurally bind and interact with associated molecules and within their environment.
AI/ML are methods for computers to learn, reason, and analyze complex data and inputs. PNNL has a long history in developing ML methods for a variety of applications and is a leader in emerging biological applications using AI.
Researchers can leverage AI/ML capabilities at PNNL to enable previously difficult tasks, including:
- Analysis of multi-omics data
- Prediction of patient outcomes
- Drug response models
- Prediction of protein structure from sequence
- Automated summarization of complex scientific ideas from literature
- Natural language interfaces to perform complex analyses and queries enabling science
Models of biological systems provide frameworks for understanding the system, allow prediction of future behavior, and generate hypotheses about mechanisms of action.
PNNL has deep expertise in biological systems modeling, including:
- High level process models
- Metabolic models
- Mechanistic models applied to a large variety of problems
PNNL can help researchers to:
- Choose appropriate models for their system, data, and questions, then
- Incorporate data from their experiments and refine the models by comparing predictions with observed outcomes.