Deep learning the cis-regulatory code of chromatin dynamics during cellular reprogramming Article Swipe
Surag Nair
,
Mohamed Ameen
,
Laksshman Sundaram
,
Anusri Pampari
,
Jacob Schreiber
,
Akshay Balsubramani
,
Yu Xin Wang
,
David Burns
,
Helen M. Blau
,
Ioannis Karakikes
,
Ke Wang
,
Anshul Kundaje
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.10.04.560808
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.10.04.560808
The concentration and stoichiometry of transcription factors (TFs) determine cellular identity and can be manipulated to drive cell state transitions. Understanding how changes in TF concentration regulate chromatin state and expression across cell state transitions remains a challenge. We investigated this relationship by profiling chromatin accessibility and gene expression at single-cell resolution across a densely sampled time course of reprogramming human fibroblasts to induced pluripotent stem cells via ectopic expression of OCT4, SOX2, KLF4, and MYC (OSKM). Using deep learning sequence models of base-resolution chromatin accessibility profiles across cell states, we deciphered predictive transcription factor (TF) motif syntax in regulatory elements, inferred affinity- and concentration-dependent dynamics of TF footprints, linked peaks to putative target genes, and elucidated rewiring of cis-regulatory networks. Our models reveal that early in reprogramming, OSK, at supraphysiological concentrations, rapidly open transient regulatory elements by occupying non-canonical low-affinity binding sites. As OSK concentration falls, the accessibility of these transient elements decays as a function of motif affinity. We find that these OSK-dependent transient elements sequester the somatic TF AP-1. This redistribution is strongly associated with the silencing of fibroblast-specific genes within individual nuclei. Together, our integrated single-cell resource and models reveal insights into the cis-regulatory code of reprogramming at unprecedented resolution. We establish a quantitative, predictive framework that links TF stoichiometry, motif syntax, and somatic silencing to provide new perspectives on the control of cell identity by TFs during fate transitions.
Related Topics
Concepts
Reprogramming
Biology
Chromatin
Transcription factor
Induced pluripotent stem cell
Cell biology
Somatic cell
Cell fate determination
KLF4
Gene silencing
SOX2
Gene
Genetics
Embryonic stem cell
Computational biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.10.04.560808
- https://www.biorxiv.org/content/biorxiv/early/2023/10/21/2023.10.04.560808.full.pdf
- OA Status
- green
- Cited By
- 38
- References
- 95
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387372690
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387372690Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.10.04.560808Digital Object Identifier
- Title
-
Deep learning the cis-regulatory code of chromatin dynamics during cellular reprogrammingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-04Full publication date if available
- Authors
-
Surag Nair, Mohamed Ameen, Laksshman Sundaram, Anusri Pampari, Jacob Schreiber, Akshay Balsubramani, Yu Xin Wang, David Burns, Helen M. Blau, Ioannis Karakikes, Ke Wang, Anshul KundajeList of authors in order
- Landing page
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https://doi.org/10.1101/2023.10.04.560808Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/10/21/2023.10.04.560808.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/10/21/2023.10.04.560808.full.pdfDirect OA link when available
- Concepts
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Reprogramming, Biology, Chromatin, Transcription factor, Induced pluripotent stem cell, Cell biology, Somatic cell, Cell fate determination, KLF4, Gene silencing, SOX2, Gene, Genetics, Embryonic stem cell, Computational biologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
38Total citation count in OpenAlex
- Citations by year (recent)
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2025: 22, 2024: 14, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
95Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sequence | 80 |
| abstract_inverted_index.strongly | 175 |
| abstract_inverted_index.Together, | 186 |
| abstract_inverted_index.affinity- | 102 |
| abstract_inverted_index.affinity. | 159 |
| abstract_inverted_index.chromatin | 27, 44, 84 |
| abstract_inverted_index.determine | 8 |
| abstract_inverted_index.elements, | 100 |
| abstract_inverted_index.establish | 205 |
| abstract_inverted_index.framework | 209 |
| abstract_inverted_index.networks. | 120 |
| abstract_inverted_index.occupying | 138 |
| abstract_inverted_index.profiling | 43 |
| abstract_inverted_index.sequester | 167 |
| abstract_inverted_index.silencing | 179, 218 |
| abstract_inverted_index.transient | 134, 151, 165 |
| abstract_inverted_index.associated | 176 |
| abstract_inverted_index.challenge. | 37 |
| abstract_inverted_index.deciphered | 91 |
| abstract_inverted_index.elucidated | 116 |
| abstract_inverted_index.expression | 30, 48, 69 |
| abstract_inverted_index.individual | 184 |
| abstract_inverted_index.integrated | 188 |
| abstract_inverted_index.predictive | 92, 208 |
| abstract_inverted_index.regulatory | 99, 135 |
| abstract_inverted_index.resolution | 51 |
| abstract_inverted_index.fibroblasts | 61 |
| abstract_inverted_index.footprints, | 108 |
| abstract_inverted_index.manipulated | 14 |
| abstract_inverted_index.pluripotent | 64 |
| abstract_inverted_index.resolution. | 203 |
| abstract_inverted_index.single-cell | 50, 189 |
| abstract_inverted_index.transitions | 34 |
| abstract_inverted_index.investigated | 39 |
| abstract_inverted_index.low-affinity | 140 |
| abstract_inverted_index.perspectives | 222 |
| abstract_inverted_index.relationship | 41 |
| abstract_inverted_index.transitions. | 19, 233 |
| abstract_inverted_index.OSK-dependent | 164 |
| abstract_inverted_index.Understanding | 20 |
| abstract_inverted_index.accessibility | 45, 85, 148 |
| abstract_inverted_index.concentration | 1, 25, 145 |
| abstract_inverted_index.non-canonical | 139 |
| abstract_inverted_index.quantitative, | 207 |
| abstract_inverted_index.reprogramming | 59, 200 |
| abstract_inverted_index.stoichiometry | 3 |
| abstract_inverted_index.transcription | 5, 93 |
| abstract_inverted_index.unprecedented | 202 |
| abstract_inverted_index.cis-regulatory | 119, 197 |
| abstract_inverted_index.redistribution | 173 |
| abstract_inverted_index.reprogramming, | 127 |
| abstract_inverted_index.stoichiometry, | 213 |
| abstract_inverted_index.base-resolution | 83 |
| abstract_inverted_index.concentrations, | 131 |
| abstract_inverted_index.supraphysiological | 130 |
| abstract_inverted_index.fibroblast-specific | 181 |
| abstract_inverted_index.concentration-dependent | 104 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5083952534 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I97018004 |
| citation_normalized_percentile.value | 0.97604325 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |