The VISION Project The VISION project conducted a ValIdated Systematic IntegratiON of epigenetic datasets across progenitor and differentiated blood cell types in mouse and human (Heuston et al. 2018, Xiang et al. 2020, Xiang et al. 2024). The project was carried out by an international group of scientists funded by the National Institute of Diabetes, Digestive, and Kidney Diseases of the National Institutes of Health (grant R24DK106766) and with intramural support from the National Human Genome Research Institute. Key products and results of the project can be visualized on the UCSC Genome Browser using this track hub. The project website provides other servers, databases, and data downloads.
RNA-seq The level of production of stable RNA from genes or any genomic interval can be measured by RNA-seq. In this technique, the RNA is converted to complementary DNA by reverse transcriptase followed by second strand synthesis, which generates a DNA copy of the sequence of the RNA. After sequencing these DNA copies of the RNA, the sequencing reads are mapped to the reference genome and/or a reference transcriptome, and the number of reads mapping to a gene or genomic interval is counted, normalized, and used as an estimate of the level of expression.
Many different procedures have been developed for RNA-seq, and these methods generate different results. In the VISION project, we generated RNA-seq data from total RNA and from polyA+ RNA from subsets mouse blood cells and cell lines. For many of these data sets, the procedures distinguished between RNA from each of the two strands of DNA; these results are called stranded or directional. Other procedures did not distinguish between reads from each strand; these results are called unstranded or non-directional.
We also imported published data from Lara-Astiaso et al. (2014) to obtain RNA-seq data on additional blood cell types.
This supertrack contains four composite tracks, with RNA-seq data produced by different methods in each of the first three composite tracks:
The options for data display differ depending on the composite track chosen; see the Track Settings page for each composite track.
The track names give an abbreviation for the blood cell type or cell line. Biological replicates are distinguished by a VISION sample id, a 3- or 4-digit number, at the beginning of the track name.
Mouse primary blood cells purified predominantly using cell surface markers include: LSK = Lin-Sca1+Kit+ cells from mouse bone marrow containing hematopoietic stem and progenitor cells, CMP = common myeloid progenitor cell, MEP = megakaryocyte-erythrocyte progenitor cell, ERY = erythroblast, GMP = granulocyte monocyte progenitor cell, MON = monocyte, NEU = neutrophil, CLP = common lymphoid progenitor cell, B = B cell, NK = natural killer cell, T_CD4 = CD4+ T cell, T_CD8 = CD8+ T cell, CFUE = colony forming unit erythroid, fl = designates ERY derived from fetal liver, ad = designates ERY derived from adult bone marrow, CFUMK = colony forming unit megakaryocyte, iMK = immature megakaryocyte, MK_fl = megakaryocyte derived from fetal liver.
Data from several immortalized cell lines were included. The G1E cells are an immortalized, GATA1-null cell line derived from mouse embryonic stem cells by gene targeting; these cells proliferate in culture as immature erythroid progenitor cells (Weiss, Yu, Orkin 1997). A stable subline of these cells, called G1E-ER4, undergoes terminal erythroid maturation when GATA1 function is restored as an activatable fusion of GATA1 to the ligand-binding domain of the estrogen receptor (ER). Untreated G1E-ER4 cells, carrying the inactive GATA1-ER, proliferate without differentiation, but treatment with estradiol (E2) activates the hybrid protein, effectively complementing the GATA1 loss-of-function and allowing synchronous erythroid differentiation and maturation (Gregory et al. 1999). An additional cell line model used here are murine erythroleukemia (MEL) cells, which can be chemically induced to mature into erythroblast-like cells with increased hemoglobin (iMEL). CH12 cells are an immortalized line that is a model for mouse B cells; the epigenetic data on CH12 cells were used to generate the B cell epigenetic state annotation.
Methods for RNA-seq are described in Jain et al. (2015), Paralkar et al. (2016), Heuston et al. (2018), Xiang et al. (2020), and Mishra et al. (2025).
Belinda Giardine generated the tracks displayed and developed the track hub.
Gregory T, Yu C, Ma A, Orkin SH, Blobel GA, Weiss MJ. GATA-1 and erythropoietin cooperate to promote erythroid cell survival by regulating bcl-xL expression. Blood. 1999; 94:87-96. PMID: 10381501.
Heuston EF, Keller CA, Lichtenberg J, Giardine B, Anderson SM; NIH Intramural Sequencing Center; Hardison RC, Bodine DM. Establishment of regulatory elements during erythro-megakaryopoiesis identifies hematopoietic lineage-commitment points. Epigenetics Chromatin. 2018 May 28;11(1):22. PMID: 29807547; PMCID: PMC5971425.
Jain D, Mishra T, Giardine BM, Keller CA, Morrissey CS, Magargee S, Dorman CM, Long M, Weiss MJ, Hardison RC. Dynamics of GATA1 binding and expression response in a GATA1-induced erythroid differentiation system. Genom Data. 2015 Jun 1;4:1-7. doi: 10.1016/j.gdata.2015.01.008. PMID: 25729644; PMCID: PMC4338950.
Mishra T, Giardine BM, Morrissey CS, Keller CA, Heuston EF, Anderson SM, Paralkar VR, Pimkin M, Weiss MJ, Bodine DM, Hardison RC. Divergence between transcriptomes and chromatin accessibility during differentiation from a bipotential progenitor cell population to erythroblasts and megakaryocytes. bioRxiv [Preprint]. 2025 Jul 3:2025.06.30.662383. doi: 10.1101/2025.06.30.662383. PMID: 40631103; PMCID: PMC12236744.
Paralkar VR, Taborda CC, Huang P, Yao Y, Kossenkov AV, Prasad R, Luan J, Davies JO, Hughes JR, Hardison RC, Blobel GA, Weiss MJ. Unlinking an lncRNA from Its Associated cis Element. Mol Cell. 2016 Apr 7;62(1):104-10. doi: 10.1016/j.molcel.2016.02.029. Epub 2016 Mar 31. PMID: 27041223; PMCID: PMC4877494.
Pinto do O P, Richter K, Carlsson L. Hematopoietic progenitor/stem cells immortalized by Lhx2 generate functional hematopoietic cells in vivo. Blood. 2002 Jun 1;99(11):3939-46. doi: 10.1182/blood.v99.11.3939. PMID: 12010792.
Weiss MJ, Yu C, Orkin SH. Erythroid-cell-specific properties of transcription factor GATA-1 revealed by phenotypic rescue of a gene-targeted cell line. Mol Cell Biol. 1997; 17:1642-1651. PMID: 9032291; PMCID: PMC231889.
Xiang G, Keller CA, Heuston E, Giardine BM, An L, Wixom AQ, Miller A, Cockburn A, Sauria MEG, Weaver K, Lichtenberg J, Göttgens B, Li Q, Bodine D, Mahony S, Taylor J, Blobel GA, Weiss MJ, Cheng Y, Yue F, Hughes J, Higgs DR, Zhang Y, Hardison RC. An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis. Genome Res. 2020 Mar;30(3):472-484. PMID: 32132109; PMCID: PMC7111515.
Xiang G, He X, Giardine BM, Isaac KJ, Taylor DJ, McCoy RC, Jansen C, Keller CA, Wixom AQ, Cockburn A, Miller A, Qi Q, He Y, Li Y, Lichtenberg J, Heuston EF, Anderson SM, Luan J, Vermunt MW, Yue F, Sauria MEG, Schatz MC, Taylor J, Göttgens B, Hughes JR, Higgs DR, Weiss MJ, Cheng Y, Blobel GA, Bodine DM, Zhang Y, Li Q, Mahony S, Hardison RC. Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes. Genome Res. 2024 Aug 20;34(7):1089-1105. PMID: 38951027; PMCID: PMC11368181.
These data are available for use without restrictions.
Ross Hardison rch8@psu.edu