Refining transcriptional programs in kidney development by integration of deep RNA-sequencing and array-based spatial profiling.
IF: 4.547
Cited by: 27


The developing mouse kidney is currently the best-characterized model of organogenesis at a transcriptional level. Detailed spatial maps have been generated for gene expression profiling combined with systematic in situ screening. These studies, however, fall short of capturing the transcriptional complexity arising from each locus due to the limited scope of microarray-based technology, which is largely based on "gene-centric" models. To address this, the polyadenylated RNA and microRNA transcriptomes of the 15.5 dpc mouse kidney were profiled using strand-specific RNA-sequencing (RNA-Seq) to a depth sufficient to complement spatial maps from pre-existing microarray datasets. The transcriptional complexity of RNAs arising from mouse RefSeq loci was catalogued; including 3568 alternatively spliced transcripts and 532 uncharacterized alternate 3' UTRs. Antisense expressions for 60% of RefSeq genes was also detected including uncharacterized non-coding transcripts overlapping kidney progenitor markers, Six2 and Sall1, and were validated by section in situ hybridization. Analysis of genes known to be involved in kidney development, particularly during mesenchymal-to-epithelial transition, showed an enrichment of non-coding antisense transcripts extended along protein-coding RNAs. The resulting resource further refines the transcriptomic cartography of kidney organogenesis by integrating deep RNA sequencing data with locus-based information from previously published expression atlases. The added resolution of RNA-Seq has provided the basis for a transition from classical gene-centric models of kidney development towards more accurate and detailed "transcript-centric" representations, which highlights the extent of transcriptional complexity of genes that direct complex development events.

MeSH terms

Alternative Splicing
High-Throughput Nucleotide Sequencing
RNA, Antisense
RNA, Messenger
Sequence Analysis, RNA
Transcription, Genetic


Thiagarajan, Rathi D
Cloonan, Nicole
Gardiner, Brooke B
Mercer, Tim R
Kolle, Gabriel
Nourbakhsh, Ehsan
Wani, Shivangi
Tang, Dave
Krishnan, Keerthana
Georgas, Kylie M
Rumballe, Bree A
Chiu, Han S
Steen, Jason A
Mattick, John S
Little, Melissa H
Grimmond, Sean M

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