Dataset of TWIST1-regulated genes in the cranial mesoderm and a transcriptome comparison of cranial mesoderm and cranial neural crest

This article contains data related to the research article entitled “Transcriptional targets of TWIST1 in the cranial mesoderm regulate cell-matrix interactions and mesenchyme maintenance” by Bildsoe et al. (2016) [1]. The data presented here are derived from: (1) a microarray-based comparison of sorted cranial mesoderm (CM) and cranial neural crest (CNC) cells from E9.5 mouse embryos; (2) comparisons of transcription profiles of head tissues from mouse embryos with a CM-specific loss-of-function of Twist1 and control mouse embryos collected at E8.5 and E9.5; (3) ChIP-seq using a TWIST1-specific monoclonal antibody with chromatin extracts from TWIST1-expressing MDCK cells, a model for a TWIST1-dependent mesenchymal state.


Value of the data
The data set provides an important reference for all studies investigating Twist1 function in the context of development and cancer.
By comparing the transcriptome of the cranial mesoderm and cranial neural crest, the data set provide a useful tool for studying the complex process of craniofacial development.
The data set potentially contributes to the identification of genes that control the mesenchymal cell state in development and cancer.

Isolation and analysis of CM and CNC populations
Embryo were collected at E9.5 from Mesp1-Cre x Z/EG (for CM) and Wnt1-Cre x Z/EG (for CNC) [2][3][4]. Heads were dissected below the first branchial arch, dissociated and prepared for cell sorting as described [2]. Each sample yielded 4000-18,000 GFP-positive cells, which were stored at À 80°C. RNA was extracted and amplified using Illumina TotalPrep (Ambion) and labeled using MessageAmp II aRNA (Ambion) as described elsewhere [1].

Chromatin Immunoprecipitation
ChIP was carried out using extracts of TWIST1-expressing MDCK cells [8]. Cross-linking in 1% formaldehyde, lysis and sonication were carried out as described [1]. Extracts were pre-cleared by incubation with A/G magnetic beads (Dynal) for 3 hrs and incubated with an anti-TWIST1 monoclonal antibody (Abcam ab50887) overnight at 4°C, before adding blocked beads and subsequent washing steps in RIPA buffer, RIPA/NaCl buffer and LiCL buffer [1]. Sequencing was carried out by the Australian Genome Research Facility.

Data analysis
Raw microarray data were log 2 transformed, quantile normalized and differential expression analyzed using the Linear Models for Microarray (LIMMA, [9] implementation within Gene Pattern. Differentially expressed genes were filtered on a false discovery rate (FDR) of 0.05.
For ChIP-Seq data, 50 bp reads were trimmed using Cutadapt [10], filtered by quality score and aligned to the CanFam3 dog genome using bowtie2 [11] as described [1]. Peaks were called using MACS2 [12] and IDR analysis performed using an IDR cut-off of 0.05. Peak coordinates from two replicates were merged, using the most extreme start and end positions of the two replicates. The equivalent mouse genome (mm10) peak genomic locations were determined using Liftover (NCBI) annotated using the R library ChipSeeker.

Acknowledgments
We thank the staff of the CMRI BioResources Unit for animal husbandry. Our work was supported by the National Health and Medical Research Council (NHMRC), Australia (Grant ID 1066832), the Australian Research Council, Australia (Grant DP 1094008) and Mr James Fairfax. HB was supported by an NHMRC Biomedical Postgraduate Scholarship and a CMRI Scholarship, XCF is supported by a University of Sydney International Postgraduate Research Scholarship; an Australian Postgraduate Award and a CMRI Scholarship and AA was supported by a visiting studentship from CMRI and an internship from the University of Groningen, The Netherlands. PPLT is an NHMRC Senior Principal Research Fellow (Grant ID 1003100, 1110751). The Flow Cytometry Centre is supported by Westmead Institute for Medical Research, Australia, NHMRC of Australia and Cancer Institute, NSW, Australia.

Transparency document. Supporting information
Transparency data associated with this article can be found in the online version at http://dx.doi. org/10.1016/j.dib.2016.09.001.