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--- 4dn:phase2:working_groups:4dnsc4all [2022/07/19 08:14]
lbintu [Primary Experimental Technologies – 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.]
+++ 4dn:phase2:working_groups:4dnsc4all [2025/04/22 16:21] (current)
@@ Line 6: / Line 6: @@
 **4DN Calendar: ** [[https://wiki.4dnucleome.org/4dn:calendar_4dn|https://wiki.4dnucleome.org/4dn:calendar_4dn]] ([[https://ics.teamup.com/feed/ksrqw1wb728dtrxdvg/8852681.ics|click here]] to add this specific WG calendar to your personal calendar)
+**Previous meeting recordings: [[https://drive.google.com/drive/u/2/folders/152onkHbb_XwmR7z5d2GwiSq1I8LabaHH|https://drive.google.com/drive/u/2/folders/152onkHbb_XwmR7z5d2GwiSq1I8LabaHH]]**
 **Zoom link:** [[https://4dnucleome-org.zoom.us/j/82077914018?pwd=RUxNcWoxeG11bEJEQjRZb3IydlZFdz09|https://4dnucleome-org.zoom.us/j/82077914018?pwd=RUxNcWoxeG11bEJEQjRZb3IydlZFdz09]]
@@ Line 14: / Line 16: @@
 **Slack channel:**
+===== Plans and Documents =====
+Agenda, Minutes, and Mission:
+[[https://docs.google.com/document/d/1PuSPeug0qgnv_-DePvDLwhbaxyYvFzeqNv_eIpDp4pA/edit|https://docs.google.com/document/d/1PuSPeug0qgnv_-DePvDLwhbaxyYvFzeqNv_eIpDp4pA/edit]]
+Meetings are on Thursdays, every other week: 10:00 – 11:00am PST (in 2022: July 28, August 11, August 25, etc).
+<font inherit/inherit;;#c0392b;;inherit>**Everyone who wants to learn more or contribute ideas, data, analysis is welcome!**</font>
 ===== Overall Mission =====
@@ Line 24: / Line 38: @@
 ===== Deliverables =====
-  - Address a few key 4DN-related biological questions in human tissues over time
+  * 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
+  * 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
+  * Develop integrative and generalizable computational solutions for 4DN analysis in tissues
 **Deliverables will be organized in two stages: **
@@ Line 39: / Line 53: @@
 ===== Key Biological Questions =====
-  - 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?
+  * 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?
+  * 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)?
+  * 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)?
+  * 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?
+  * Comparative analysis: How do 3D genome features of a neural cell type in tissue compare to 2D iPS-neurons and neurons in organoids?
-===== Primary Experimental Technologies – 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. =====
+===== Primary Experimental Technologies  =====
-  - 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).
+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:
-  - 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)
+  * 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.
-=====   Primary Experimental Systems – We aim to inclusively implement the primary technologies mentioned above across the following three physiologically important systems:   =====
+===== Primary Experimental Systems =====
+We aim to inclusively implement the primary technologies mentioned above across the following physiologically important systems:
 **Stage 1 – Pilot Phase**
-  *
+  - **Brain (including human iPS-neurons during neural stimulation, human iPS-organoids, fetal brain tissue, and human adult brain tissues): Leads: Cremins, Ren, Shen **
+  - **Heart (human iPS-cardiomyocytes, human heart tissue): Lead: Bruneau**
-**Brain (including human iPS-neurons during neural stimulation, human iPS-organoids, fetal brain tissue, and human adult brain tissues): Leads: Cremins, Ren, Shen **
+Note that it is important to add the temporal dimension. iPS-derived neurons and cardiomyocytes are perfect for temporal perturbations
-  *
-**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**
-  *
+  - **Mouse fetal development**
+  - **Mouse embryonic heart tissue**
+  - **Mouse fetal and adult brain tissue**
+  - **Human iPS-derived pancreatic organoids**
-**Mouse fetal development**
-  *
-**Mouse embryonic heart tissue**
+===== Computational Analysis and Method Development Opportunities =====
-  *
-**Mouse fetal and adult brain tissue**
+Computational integration, analysis, and modeling should be at the forefront along with the experimental processes. Major deliverable will include software tools, benchmark datasets and metrics, and analysis workflows to establish the “navigable maps” of nuclear architecture:
-  *
-**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)
-  *
-**[those interested please fill in here]**
+  * Integrate genomics and imaging data by combining single-cell data and population data
-=====   Computational Analysis and Method Development Opportunities – Computational integration, analysis, and modeling should be at the forefront along with the experimental processes. Major deliverable will include software tools, benchmark datasets and metrics, and analysis workflows to establish the “navigable maps” of nuclear architecture:   =====
-  *
+  * Unveil the connections between nuclear structure and function (e.g., transcription) in single cells, and their temporal patterns
-**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)**
+  * Predictive modeling to identify and prioritize key players (sequence elements and regulators) that drive nuclear structure
-  *
-**Integrate genomics and imaging data by combining single-cell data and population data**
+  * Modeling approaches to assess mechanisms of nuclear structure in space and time
-  *
-**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**
-===== Plans and Documents =====

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