NIH-HCA 2020 Joint Meeting

Agenda

Day 1: March 30, 2020
9:30 -9:35Opening
9:35 - 10:05 COVID-19 Collaborations
10:05 - 10:30Day 1 Opening Plenary
10: 30 - 11:15Breakout Session #1, Part 1
11:15 - 11:30Welcoming Remarks: NIH Director Francis Collins
11:30 - 11:45Break
11:45 - 1:00Breakout Session #1, Part 2
1:00 - 1:45Lunch Break
1:45 - 1:50Plenary
1:50 - 2:50Breakout Session #2, Part 1
2:50 - 3:05Break
3:05 - 4:05Breakout Session #2, Part 2
4:05 - 4:30Day 1 Closing Plenary
 
Day 2: March 31, 2020
10:00 - 10:15Day 2 Opening Plenary
10:15 - 11:00Breakout Session #3, Part 1
11:00 - 11:15Break
11:15 - 12:30Breakout Session 3, Part #2
12:30 - 1:15Lunch Break
1:15 - 1:20Plenary Session
1:20 - 2:05Breakout Session #4, Part 1
2:05 - 2:20Break
2:20 - 3:35Breakout Session #4, Part 2
3:35 - 4:30Day 2 Closing Plenary

Breakout Sessions

Clinical Metadata   
Data Architecture and Integration    Temporal Analysis: Development and Pediatric    Multiplex Molecular Profiling Tools    Spatial Profiling Tools   
a. What is the scope of "clinical" metadata? Developing a roadmap of establishing “clinical” metadataa. Common interfaces There are several different cell atlas’ing initiatives which are all building portals and storage solutions for their data. What common interfaces are needed to minimize any access barriers across multiple projectsa. Organ-based or anatomical unit-based atlas How do we achieve V1 development atlasa. Imaging-based techniques Imaging- based techniques at all scales for multimodal molecular profilinga. Antibody-based imaging methods e.g., staining using fluorochromes, metal-chelates, etc., in an antibody-based manner
b. Core clinical metadata How do we achieve core clinical metadata standards across diverse tissue types, tissue collection methods, and tissue collection sites. (sample level vs patient level)?b. Data Storage and Data Movementb. Engaging developmental biology community Expertise in developmental biologyb. Single-cell sequencing-based techniques Spanning the Central Dogmab. Imaging-based transcriptomics methods e.g., multiplexed FISH and in situ sequencing methods
c. Levels of Metadata How to manage the clinical metadata data outside of the core?c. Data format standardsc. What biology can we learn from development atlas What are important questions?c. Sequencing-based spatial measurements (e.g. ST)
d. Review process for Clinical Metadata The process to gain consensusd. Authentication for accessd. Relevance of development to pediatric and adult health/diseased. Multi-omic spatial data and integratio
e. Sample ID Naming Conventions Naming conventions to account for patient or subject, multiple samples per subject, many time points (longitudinal studies) and spatial attributese. Ethics relating to development atlas
f. Age resolution for pediatric and development atlas
Common Coordinate Frameworks    Metadata - Schemas & Ontologies    Tissue Collection & Processing    Multiplex Molecular Profiling Analysis    Spatial Profiling Analysis   
a. CCF User Interfaces Major anatomical terms and 3D structures in human, Major human cell types, sizes, and “calling cards”, Taxonomy nomenclatures and standardizationa. Common metadata schemas and their use casesa. Tissue types and purpose What type of tissue are you collecting and for what purpose?a. Computational Challenges Given a set of multimodal assays, what are computational challenges in processing and extracting data from these multiplexed molecular profiles (e.g. ATAC-seq and RNA-seq)?a. Highly multiplexed image analysis (antibody-based: e.g. CyCIF, IMC, MIBI, CODEX etc.)
b. The role of computational physiologyb. Federation of metadata standards efforts Goals for collaboration and unificationb. Characterization of tissue processingb. Standards and provenance What process can be used to standardize data, metadata, analyses, and provenance to facilitate sharing?



b. Analysis and comparison of transcriptomics in-situ data e.g., MERFISH, Slide-seq, Spatial Transcriptomics, Visium, ISS, etc.
c. The role of Functional Tissue Units (FTUs)c. Anatomy and CCF in metadata How should we reference anatomy and common coordinate frameworks in metadata?c. Measures for QA/QC What are the measures you take for quality assessment and control of the tissue collected?c. Pipeline Validation and Dissemination What could be done to validate pipelines?c. Generation of 3D reference volumes & frameworks Generation of 3D reference volumes / frameworks of different tissues for registering spatial data
d. Data localizations to CCFd. Cell Ontologies What do we need from cell ontologies and how can we extend them in an age of data-driven cell type definition.d. What multiplexed data are available today?
Sharing & Standardizing Biospecimens & Experimental Methods   
Data QA/QC   
Atlas Integration   
Affinity Reagent Development and Standards   
Data Modeling & Integration   
a. Biospecimen access to enable joint analysisa. Bespoke QA/QC vs Standardizationa. Developing multiscale cell and tissue taxonomy concepts a. Affinity reagents for organ-scale cell phenotypinga. Multi-omics data analysis/integration
b. scRNA-seq and snRNA-seq approaches Both scRNA-seq and snRNA-seq approaches generate libraries for sequencing and could be additional biospecimens for sharingb. Common Metricsb. Defining data types for each taxonomy level b. Affinity reagent validation in consortiab. Network modeling
c. Making protocols publicly available Experimental protocols developed from NIH-funded work must be openly available to the public now and in the future.c. Common data formats and containersc. Developing informatics and models c. Target selection and proteins of interest (POI)c. Genetic basis of cellular identity
d. QC and “validation” of sc/snRNA-seq data QC and “validation” of sc/snRNA-seq data are important to establish confidence in a datasetd. Sharing Protocols and Analysis Pipelinesd. Variation in imaging technologies Source of pre-analytical variation in imaging technologies that use affinity reagents/Ab’sd. Cellular dynamics, plasticity, perturbations
e. Biobank testing Cross-consortium sample studies need to be tested - there are many biobanks with expertise in different arease. Maintaining Repeatable Analysis Over Time
f. Common benchmarking Whether a biospecimen or a dataset, common benchmarking is necessary to establish qualityf. QA/QC of Releases
Ethics and Diversity   
FAIRness   
Outreach   
Cell Type Annotation   
Data Visualization & Querying   
a. Challenges to open data sharing how to frame international sharing of genomic data and metadata, within the limitations of ethical or legal frameworks. a. How well do you know the 15 FAIR principles? a. What outreach and engagement activities should be part of the HCA?a. Automated cell type annotation a. Types of data What types of data will we need to visualize, analyze and access via search
b. Collecting tissue samples and data Examining the issues arising from the collection of human tissues from different sources and different sampling contextsb. FAIR Data b. Using technology and virtual online spaces How can we use technology and virtual online spaces to help with outreach and engagement?b. Expert annotations: tools and initiatives b. What does integrative visualization of atlas data look like? What tools are available or need to be created for integrated visualization?
c. Defining diversity (and equity) How to frame diversity (and equity) across consortiac. FAIR Standards c. Equity: How do we know we are reaching everyone who is interested? Across countries and continents?c. Cell ontologies and terminology c. How do we integrate search across metadata, data, and coordinates? What do we want to search against and how will these fields be organized?
d. Equity in action What are some of the actions taken to implement equity and diversityd. Best Practices d. How do we fund outreach activities?d. Integrating annotation across data modalities d. How can we analyze distributed datasets? How image and other datasets can be accessed and analyzed
e. Interactions between ethics and equity Potential overlap and intended interactions with the ethics working group (EWG)e. Measuring Progress e. Outreach roadmap: What are our priorities?e. Operationse. CCF and synthetic data incorporation How would we incorporate a common coordinate framework and synthetic data into search, visualization, and analysis?

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