A Novel Dual Eigen-Analysis of Mouse Multi-Tissues' Expression Profiles Unveils New Perspectives into Type 2 Diabetes

Spatially Resolved Transcriptomes of Mammalian Kidneys Illustrate the Molecular Complexity and Interactions of Functional Nephron Segments

Available transcriptomes of the mammalian kidney provide limited information on the spatial interplay between different functional nephron structures due to the required dissociation of tissue with traditional transcriptome-based methodologies. A deeper understanding of the complexity of functional nephron structures requires a non-dissociative transcriptomics approach, such as spatial transcriptomics sequencing (ST-seq). We hypothesize that the application of ST-seq in normal mammalian kidneys will give transcriptomic insights within and across species of physiology at the functional structure level and cellular communication at the cell level. Here, we applied ST-seq in six mice and four human kidneys that were histologically absent of any overt pathology. We defined the location of specific nephron structures in the captured ST-seq datasets using three lines of evidence: pathologist's annotation, marker gene expression, and integration with public single-cell and/or single-nucleus RNA-sequencing datasets. We compared the mouse and human cortical kidney regions. In the human ST-seq datasets, we further investigated the cellular communication within glomeruli and regions of proximal tubules-peritubular capillaries by screening for co-expression of ligand-receptor gene pairs. Gene expression signatures of distinct nephron structures and microvascular regions were spatially resolved within the mouse and human ST-seq datasets. We identified 7,370 differentially expressed genes (p adj < 0.05) distinguishing species, suggesting changes in energy production and metabolism in mouse cortical regions relative to human kidneys. Hundreds of potential ligand-receptor interactions were identified within glomeruli and regions of proximal tubules-peritubular capillaries, including known and novel interactions relevant to kidney physiology. Our application of ST-seq to normal human and murine kidneys confirms current knowledge and localization of transcripts within the kidney. Furthermore, the generated ST-seq datasets provide a valuable resource for the kidney community that can be used to inform future research into this complex organ.

MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

Background

Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable.

Results

We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren.

Conclusions

MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04288-0.