Chairs: Lacra Bintu, Jennifer Cremins, Yin Shen, Bing Ren, Jian Ma and Benoit Bruneau

4DN Google Drive folder: https://drive.google.com/drive/folders/1mRUxJphkf9F5sHgtg0kSS-eMocDv520I?usp=sharing

4DN Calendar: https://wiki.4dnucleome.org/4dn:calendar_4dn (click here to add this specific WG calendar to your personal calendar)

Zoom link: https://4dnucleome-org.zoom.us/j/82077914018?pwd=RUxNcWoxeG11bEJEQjRZb3IydlZFdz09

Meeting attendee spreadsheet: https://docs.google.com/spreadsheets/d/1bDY7k0iBSWa3hgWsB5MWL5oquZpVxdEKsR4aKjuIQko/edit?usp=sharing

Email list: 4dnsc4all@4dnucleome.org

Slack channel:

Agenda and Minutes:

https://docs.google.com/document/d/1PuSPeug0qgnv_-DePvDLwhbaxyYvFzeqNv_eIpDp4pA/edit

Why Act? We aim to develop a consortium-wide collaborative project in Phase 2 to demonstrate a clear long-term legacy that resonates with the broad community.

Why Now? There has been burgeoning discussions and demand from the consortium:”'What are we delivering in terms of meaningful long-term insight and data?” It’s time to use the following three years or so to tackle a consortium-wide grand challenge.

• Address a few key 4DN-related biological questions in human tissues over time

• Produce datasets by harnessing existing single-cell multimodal methods as well as bulk assays represented in the consortium

• Develop integrative and generalizable computational solutions for 4DN analysis in tissues

Deliverables will be organized in two stages:

• Stage 1: Focused on the brain and heart. Demonstrate technologies, data generation, computational analysis, modeling.

• Stage 2: Apply to a broader set of biological systems.

• How are chromatin fibers organized as “multiple scales of 3D genome features” (“3D genome features” hereafter), i.e., territories, A/B compartments, nuclear bodies, TADs, subTADs, loops, chromatin interactions with proteins/RNAs, in individual cells of the brain, heart, embryonic tissues, etc? Are general 3D genome features observed at the bulk level observable in individual cells?

• Models with testable predictions for long-term functional perturbative studies: How do 3D genome features govern mRNA levels and transcriptional bursting properties in single cells? How do enhancers regulate promoters through physical contacts in single cells?

• How does cell-to-cell variability of 3D genome features give rise to population-based structural and functional (gene expression, replication) observations? How does such variability of 3D genome features in different cell types provide for understanding developmental programs and disease pathogenesis in different organ systems (brain, heart, etc)?

• Time dimension: To what extent do 3D genome features change as a response to time-based stimulations (e.g. neural activity stimulation of iPSC-neurons, growth factor/hormone-stimulation of cardiomyocytes)?

• Comparative analysis: How do 3D genome features of a neural cell type in tissue compare to 2D iPS-neurons and neurons in organoids?

Two primary single-cell technologies (imaging and omics) and their integration with existing assays (including population-based methods) to reach a more complete view of nuclear organization over time:

• Single-cell multiomics: single nucleus methyl 3C seq (snm3C-seq) for combined DNA methylation and Hi-C in single cells, supplemented with single-cell multiomics (e.g., combined ATAC/RNA-seq).

• Nuclear organization imaging: sequential DNA FISH (Oligopaints) imaging for chromatin (whole genome or targeted loci with 5kb steps) combined with sequential IF for nuclear bodies (using widefield we can likely “see” features separated by at least 100 kb distance)

A major effort will be in the computational integration of these primary single-cell measurements with single-cell functional data (e.g., scRNA-seq, multiplexed RNA FISH), other bleeding edge single-cell technologies and population-based assays for mapping 4DN in tissues over development and lifespan in several organ systems.

We aim to inclusively implement the primary technologies mentioned above across the following physiologically important systems:

Stage 1 – Pilot Phase

1. Brain (including human iPS-neurons during neural stimulation, human iPS-organoids, fetal brain tissue, and human adult brain tissues): Leads: Cremins, Ren, Shen
2. Heart (human iPS-cardiomyocytes, human heart tissue): Lead: Bruneau

Note that it is important to add the temporal dimension. iPS-derived neurons and cardiomyocytes are perfect for temporal perturbations

Stage 2 – Expansion Stage

1. Mouse fetal development
2. Mouse embryonic heart tissue
3. Mouse fetal and adult brain tissue
4. Human iPS-derived pancreatic organoids

• Algorithms for image analysis steps (fiducial localization, spot calling, segmentation, drift correction) and single-cell integrative analysis, including 3D genome feature detection (single-cell TADs/subTADs/bodies/compartments/loops)

• Integrate genomics and imaging data by combining single-cell data and population data

• Unveil the connections between nuclear structure and function (e.g., transcription) in single cells, and their temporal patterns

• Predictive modeling to identify and prioritize key players (sequence elements and regulators) that drive nuclear structure

• Modeling approaches to assess mechanisms of nuclear structure in space and time