Back

Integrative Single-cell RNA-Seq and ATAC-Seq Analysis of Human Foetal Liver and Bone Marrow Haematopoiesis

Ranzoni, A. M.; Tangherloni, A.; Berest, I.; Riva, S. G.; Myers, B.; Strzelecka, P. M.; Xu, J.; Panada, E.; Mohorianu, I.; Zaugg, J. B.; Cvejic, A.

2020-05-07 cell biology
10.1101/2020.05.06.080259 bioRxiv
Show abstract

Regulation of haematopoiesis during human development remains poorly defined. Here, we applied single-cell (sc)RNA-Seq and scATAC-Seq analysis to over 8,000 human immunophenotypic blood cells from foetal liver and bone marrow. We inferred their differentiation trajectory and identified three highly proliferative oligopotent progenitor populations downstream from haematopoietic stem cell/multipotent progenitors (HSC/MPPs). Along this trajectory, we observed opposing patterns of chromatin accessibility and differentiation that coincided with dynamic changes in the activity of distinct lineage-specific transcription factors. Integrative analysis of chromatin accessibility and gene expression revealed extensive epigenetic but not transcriptional priming of HSC/MPPs prior to their lineage commitment. Finally, we refined and functionally validated the sorting strategy for the HSC/MPPs and achieved around 90% enrichment. Our study provides a useful framework for future investigation of human developmental haematopoiesis in the context of blood pathologies and regenerative medicine.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
eLife
● 5828 papers in training set
Top 6%
12.9%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 21%
7.9%
Similar papers in this journal
3
Life Science Alliance
● 285 papers in training set
Top 0.1%
7.9%
Similar papers in this journal
4
Cell Reports
○ 1498 papers in training set
Top 4%
6.7%
Similar papers in this journal
5
iScience
○ 1154 papers in training set
Top 2%
5.5%
Similar papers in this journal
6
Stem Cell Reports
○ 130 papers in training set
Top 0.5%
4.8%
7
Communications Biology
○ 993 papers in training set
Top 2%
4.8%
50% of probability mass above
8
BMC Biology
○ 265 papers in training set
Top 0.5%
4.0%
9
The EMBO Journal
○ 309 papers in training set
Top 1%
3.5%
10
Genome Biology
○ 637 papers in training set
Top 4%
2.6%
11
Nucleic Acids Research
◐ 1281 papers in training set
Top 7%
2.4%
12
EMBO Reports
○ 263 papers in training set
Top 2%
2.4%
13
Cell Reports Methods
○ 165 papers in training set
Top 1%
2.1%
14
Science Advances
● 1243 papers in training set
Top 18%
2.0%
15
Scientific Reports
○ 3612 papers in training set
Top 49%
1.9%
16
Development
● 497 papers in training set
Top 3%
1.9%
17
Cell Stem Cell
○ 62 papers in training set
Top 1%
1.5%
18
Molecular Systems Biology
○ 162 papers in training set
Top 2%
1.1%
19
Stem Cells
◐ 31 papers in training set
Top 0.5%
1.1%
20
Blood Advances
● 62 papers in training set
Top 1.0%
1.1%
21
Biology Open
● 156 papers in training set
Top 3%
1.0%
22
Cytometry Part A
○ 33 papers in training set
Top 0.2%
1.0%
23
Advanced Science
○ 286 papers in training set
Top 9%
0.8%
24
Epigenetics & Chromatin
52 papers in training set
Top 0.6%
0.8%
25
Cell Reports Medicine
○ 153 papers in training set
Top 5%
0.8%
26
Nature
○ 645 papers in training set
Top 10%
0.8%
27
Experimental Hematology
○ 11 papers in training set
Top 0.2%
0.8%
28
Stem Cell Research
○ 16 papers in training set
Top 0.3%
0.6%
29
Haematologica
● 25 papers in training set
Top 0.7%
0.6%
30
Molecular Cell
○ 350 papers in training set
Top 5%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.