Data Descriptor: Transcriptomic analysis of gene signatures associated with sickle pain

Jinny A. Paul, Anupam Aich, Juan E. Abrahante, Ying Wang, Rebecca S. LaRue, Susan K. Rathe, Krystina Kalland, Aditya Mittal, Ritu Jha, Fei Peng, David A. Largaespada, Anindya Bagchi, Kalpna Gupta

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Pain is a hallmark feature of sickle cell disease (SCD). Recurrent and unpredictable acute pain due to vaso-oclussive crises (VOC) is unique to SCD; and can be superimposed on chronic pain. To examine the mechanisms underlying pain in SCD, we performed RNA sequencing of dorsal root ganglion (DRG) of transgenic sickle mice and their age-matched control mice expressing normal human hemoglobin A, at 2 and 5 months of age. Sickle and control mice of both ages were equally divided into hypoxia/reoxygenation (to simulate VOC) and normoxia treatment, resulting in eight groups of mice. Each group had at least six mice. RNA isolated from the DRG was sequenced and paired-end 50 bp sequencing data were generated using Illumina's HiSeq 2000. This large dataset can serve as a resource for examining transcriptional changes in the DRG that are associated with age and hypoxia/reoxygenation associated signatures of nociceptive mechanisms underlying chronic and acute pain, respectively.

Original languageEnglish (US)
Article number170051
JournalScientific Data
StatePublished - May 16 2017

Bibliographical note

Funding Information:
The authors would like to thank Ms Barb Benson for proof reading the manuscript, the University of Minnesota Genomics Center for sequencing the RNA samples and Aaron Becker for quality control and suggestions on the Methods section. The authors would also like to thank the Institute for Engineering in Medicine, University of Minnesota and NIH grants, RO1 103773 and UO1 HL117664 to K.G. for funding support. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Kalpna Gupta, PhD had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Publisher Copyright:
© The Author(s) 2017.


Dive into the research topics of 'Data Descriptor: Transcriptomic analysis of gene signatures associated with sickle pain'. Together they form a unique fingerprint.

Cite this