A reference tissue atlas for the human kidney.
Jens HansenRachel SealfonRajasree MenonMichael T EadonBlue B LakeBecky SteckKavya AnjaniSamir ParikhTara K SigdelGuanshi ZhangDušan VeličkovićDaria BarwinskaTheodore AlexandrovDejan DobiPriyanka RashmiEdgar A OttoMiguel RiveraMichael P RoseChristopher R AndertonJohn P ShapiroAnnapurna PamreddySeth WinfreeYuguang XiongYongqun Oliver HeIan H de BoerJeffrey B HodginLaura BarisoniAbhijit S NaikKumar SharmaMinnie M SarwalKun ZhangJonathan HimmelfarbBrad H RovinTarek M El-AchkarZoltan G LaszikJohn Cijiang HePierre C DagherM Todd ValeriusSanjay JainLisa M SatlinOlga G TroyanskayaMatthias KretzlerRavi IyengarEvren U Azeloglunull nullPublished in: Science advances (2022)
Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.
Keyphrases
- single cell
- rna seq
- endothelial cells
- high throughput
- induced apoptosis
- mass spectrometry
- genome wide
- induced pluripotent stem cells
- healthcare
- pluripotent stem cells
- cell cycle arrest
- public health
- machine learning
- high resolution
- ms ms
- deep learning
- stem cells
- robot assisted
- bioinformatics analysis
- endoplasmic reticulum stress
- bone marrow
- genome wide identification
- climate change
- cell death
- label free
- ultrasound guided
- nucleic acid