RT Journal Article
SR Electronic
T1 Towards Building a Smart Kidney Atlas: Network-based integration of multimodal transcriptomic, proteomic, metabolomic and imaging data in the Kidney Precision Medicine Project
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.23.216507
DO 10.1101/2020.07.23.216507
A1 Jens Hansen
A1 Rachel Sealfon
A1 Rajasree Menon
A1 Michael T. Eadon
A1 Blue B. Lake
A1 Becky Steck
A1 Dejan Dobi
A1 Samir Parikh
A1 Tara K. Sidgel
A1 Theodore Alexandrov
A1 Andrew Schroeder
A1 Edgar A. Otto
A1 Christopher R. Anderton
A1 Daria Barwinska
A1 Guanshi Zheng
A1 Michael P. Rose
A1 John P. Shapiro
A1 Dusan Velickovic
A1 Annapurna Pamreddy
A1 Seth Winfree
A1 Yongqun He
A1 Ian H. de Boer
A1 Jeffrey B. Hodgin
A1 Abhijit Nair
A1 Kumar Sharma
A1 Minnie Sarwal
A1 Kun Zhang
A1 Jonathan Himmelfarb
A1 Zoltan Laszik
A1 Brad Rovin
A1 Pierre C. Dagher
A1 John Cijiang He
A1 Tarek M. El-Achkar
A1 Sanjay Jain
A1 Olga G. Troyanskaya
A1 Matthias Kretzler
A1 Ravi Iyengar
A1 Evren U. Azeloglu
A1 for the Kidney Precision Medicine Project Consortium
YR 2020
UL http://biorxiv.org/content/early/2020/07/24/2020.07.23.216507.abstract
AB The Kidney Precision Medicine Project (KPMP) plans to construct a spatially specified tissue atlas of the human kidney at a cellular resolution with near comprehensive molecular details. The atlas will have maps of healthy, acute kidney injury and chronic kidney disease tissues. To construct such maps, we integrate different data sets that profile mRNAs, proteins and metabolites collected by five KPMP Tissue Interrogation Sites. Here, we describe a set of hierarchical analytical methods to process, combine, and harmonize single-cell, single-nucleus and subsegmental laser microdissection (LMD) transcriptomics with LMD and near single-cell proteomics, 3-D nondestructive and immunofluorescence-based Codex imaging and spatial metabolomics datasets. We use nephrectomy, healthy living donor and surveillance transplant biopsy tissues to create a harmonized reference tissue map. Our results demonstrate that different assays produce reliable and coherent identification of cell types and tissue subsegments. They further show that the molecular profiles and pathways are partially overlapping yet complementary for cell type-specific and subsegmental physiological processes. Focusing on the proximal tubules, we find that our integrated systems biologybased analyses identify different subtypes of tubular cells with potential for different levels of lipid oxidation and energy generation. Integration of our omics data with pathways from the literature, enables us to construct predictive computational models to develop a smart kidney atlas. These integrated models can describe physiological capabilities of the tissues based on the underlying cell types and pathways in health and disease.Competing Interest StatementThe authors have declared no competing interest.