RESUMO
BACKGROUND: Proximal tubule cells dominate the kidney parenchyma numerically, although less abundant cell types of the distal nephron have disproportionate roles in water and electrolyte balance. METHODS: Coupling of a FACS-based enrichment protocol with single-cell RNA-seq profiled the transcriptomes of 9099 cells from the thick ascending limb (CTAL)/distal convoluted tubule (DCT) region of the mouse nephron. RESULTS: Unsupervised clustering revealed Slc12a3 +/Pvalb + and Slc12a3 +/Pvalb - cells, identified as DCT1 and DCT2 cells, respectively. DCT1 cells appear to be heterogeneous, with orthogonally variable expression of Slc8a1, Calb1, and Ckb. An additional DCT1 subcluster showed marked enrichment of cell cycle-/cell proliferation-associated mRNAs (e.g., Mki67, Stmn1, and Top2a), which fit with the known plasticity of DCT cells. No DCT2-specific transcripts were found. DCT2 cells contrast with DCT1 cells by expression of epithelial sodium channel ß- and γ-subunits and much stronger expression of transcripts associated with calcium transport (Trpv5, Calb1, S100g, and Slc8a1). Additionally, scRNA-seq identified three distinct CTAL (Slc12a1 +) cell subtypes. One of these expressed Nos1 and Avpr1a, consistent with macula densa cells. The other two CTAL clusters were distinguished by Cldn10 and Ptger3 in one and Cldn16 and Foxq1 in the other. These two CTAL cell types were also distinguished by expression of alternative Iroquois homeobox transcription factors, with Irx1 and Irx2 in the Cldn10 + CTAL cells and Irx3 in the Cldn16 + CTAL cells. CONCLUSIONS: Single-cell transcriptomics revealed unexpected diversity among the cells of the distal nephron in mouse. Web-based data resources are provided for the single-cell data.
RESUMO
BACKGROUND: The repertoire of protein expression along the renal tubule depends both on regulation of transcription and regulation of alternative splicing that can generate multiple proteins from a single gene. METHODS: A full-length, small-sample RNA-seq protocol profiled transcriptomes for all 14 renal tubule segments microdissected from mouse kidneys. RESULTS: This study identified >34,000 transcripts, including 3709 that were expressed in a segment-specific manner. All data are provided as an online resource (https://esbl.nhlbi.nih.gov/MRECA/Nephron/). Many of the genes expressed in unique patterns along the renal tubule were solute carriers, transcription factors, or G protein-coupled receptors that account for segment-specific function. Mapping the distribution of transcripts associated with Wnk-SPAK-PKA signaling, renin-angiotensin-aldosterone signaling, and cystic diseases of the kidney illustrated the applications of the online resource. The method allowed full-length mapping of RNA-seq reads, which facilitated comprehensive, unbiased characterization of alternative exon usage along the renal tubule, including known isoforms of Cldn10, Kcnj1 (ROMK), Slc12a1 (NKCC2), Wnk1, Stk39 (SPAK), and Slc14a2 (UT-A urea transporter). It also identified many novel isoforms with segment-specific distribution. These included variants associated with altered protein structure (Slc9a8, Khk, Tsc22d1, and Scoc), and variants that may affect untranslated, regulatory regions of transcripts (Pth1r, Pkar1a, and Dab2). CONCLUSIONS: Full-length, unbiased sequencing of transcripts identified gene-expression patterns along the mouse renal tubule. The data, provided as an online resource, include both quantitative and qualitative differences in transcripts. Identification of alternative splicing along the renal tubule may prove critical to understanding renal physiology and pathophysiology.
RESUMO
Aquaporin-2 (Aqp2) gene transcription is strongly regulated by vasopressin in the renal collecting duct. However, the transcription factors (TFs) responsible for the regulation of expression of Aqp2 remain largely unknown. We used Bayes' theorem to integrate several -omics data sets to stratify the 1,344 TFs present in the mouse genome with regard to probabilities of regulating Aqp2 gene transcription. Also, we carried out new RNA sequencing experiments mapping the time course of vasopressin-induced changes in the transcriptome of mpkCCD cells to identify TFs that change in tandem with Aqp2. The analysis identified 17 of 1,344 TFs that are most likely to be involved in the regulation of Aqp2 gene transcription. These TFs included eight that have been proposed in prior studies to play a role in Aqp2 regulation, viz., Cebpb, Elf1, Elf3, Ets1, Jun, Junb, Nfkb1, and Sp1. The remaining nine represent new candidates for future studies (Atf1, Irf3, Klf5, Klf6, Mef2d, Nfyb, Nr2f6, Stat3, and Nr4a1). Conspicuously absent is CREB (Creb1), which has been widely proposed to mediate vasopressin-induced regulation of Aqp2 gene transcription (Nielsen S, Frokiaer J, Marples D, Kwon TH, Agre P, Knepper MA. Physiol Rev 82: 205-244, 2002; Kortenoeven ML, Fenton RA. Biochim Biophys Acta 1840: 1533-1549, 2014; Bockenhauer D, Bichet DG. Nat Rev Nephrol 11: 576-588, 2015; Pearce D, Soundararajan R, Trimpert C, Kashlan OB, Deen PM, Kohan DE. Clin J Am Soc Nephrol 10: 135-146, 2015). Instead, another CREB-like TF, Atf1, ranked fourth among all TFs. RNA sequencing time-course experiments showed a rapid increase in Aqp2 mRNA, within 3 h of vasopressin exposure. This response was matched by an equally rapid increase in the abundance of the mRNA coding for Cebpb, which we have shown by chromatin immunoprecipitation-sequencing studies to bind downstream from the Aqp2 gene. The identified TFs provide a roadmap for future studies to understand regulation of Aqp2 gene expression.NEW & NOTEWORTHY Abetted by the advent of systems biology-based ("-omics") techniques in the 21st century, there has been a massive expansion of published data relevant to virtually every physiological question. The authors have developed a large-scale data integration approach based on the application of Bayes'' theorem. In the current work, they integrated 12 different -omics data sets to identify the transcription factors most likely to mediate vasopressin-dependent regulation of transcription of the aquaporin-2 gene.