NIH-HCA 2020 Joint Meeting

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Agenda

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

Day 2: March 31, 2020
10:00 - 10:15	Day 2 Opening Plenary
10:15 - 11:00	Breakout Session #3, Part 1
11:00 - 11:15	Break
11:15 - 12:30	Breakout Session 3, Part #2
12:30 - 1:15	Lunch Break
1:15 - 1:20	Plenary Session
1:20 - 2:05	Breakout Session #4, Part 1
2:05 - 2:20	Break
2:20 - 3:35	Breakout Session #4, Part 2
3:35 - 4:30	Day 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” metadata	a. 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 projects	a. Organ-based or anatomical unit-based atlas How do we achieve V1 development atlas	a. Imaging-based techniques Imaging- based techniques at all scales for multimodal molecular profiling	a. 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 Movement	b. Engaging developmental biology community Expertise in developmental biology	b. Single-cell sequencing-based techniques Spanning the Central Dogma	b. 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 standards	c. 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 consensus	d. Authentication for access	d. Relevance of development to pediatric and adult health/disease		d. 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 attributes		e. 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 standardization	a. Common metadata schemas and their use cases	a. 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 physiology	b. Federation of metadata standards efforts Goals for collaboration and unification	b. Characterization of tissue processing	b. 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 CCF	d. 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 analysis	a. Bespoke QA/QC vs Standardization	a. Developing multiscale cell and tissue taxonomy concepts	a. Affinity reagents for organ-scale cell phenotyping	a. 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 sharing	b. Common Metrics	b. Defining data types for each taxonomy level	b. Affinity reagent validation in consortia	b. 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 containers	c. 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 dataset	d. Sharing Protocols and Analysis Pipelines		d. Variation in imaging technologies Source of pre-analytical variation in imaging technologies that use affinity reagents/Ab’s	d. Cellular dynamics, plasticity, perturbations
e. Biobank testing Cross-consortium sample studies need to be tested - there are many biobanks with expertise in different areas	e. Maintaining Repeatable Analysis Over Time
f. Common benchmarking Whether a biospecimen or a dataset, common benchmarking is necessary to establish quality	f. 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 contexts	b. 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 consortia	c. 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 diversity	d. 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. Operations	e. CCF and synthetic data incorporation How would we incorporate a common coordinate framework and synthetic data into search, visualization, and analysis?