Single cell resolution of human chronic kidney disease for precision medicine in nephrology

慢性肾脏疾病(CKD)影响了超过10%的德国人口，但缺乏有针对性的治疗以及精确的预后工具。我们有一个独特的人类肾脏活检生物库，拥有长达10年的广泛临床随访数据。我们的目标是通过使用冰冻的人肾活检组织中的单个核SnRNA-seq和SnATACseq来表征导致大多数CKD的10种肾脏疾病(常见和罕见)。活体肾脏捐献者的活检将作为健康对照。我们的目标是进行200例活组织检查(每种肾脏疾病类型20例)，并提供至少5年的随访数据。在20例活检中，10例肾功能在随访期间稳定，10例肾功能明显下降。我们将部署新的策略来汇集患者样本并基于SNP分析分离数据，以及针对单细胞数据量身定做的功能基因组学方法，以提取有关解除调控的途径、转录因子和其他过程的信息，以确定新的生物标记物和治疗靶点。这个雄心勃勃的跨学科项目需要肾病学家、计算和分子生物学家以及机器学习者的专业知识。只有利用DFG测序中心的资源才有可能做到这一点。推动这一项目的热情源于一个简单的假设：使用最先进的单细胞基因组学有可能找到CKD迫切需要的新的治疗靶点和预后措施。

Chronic kidney disease (CKD) affects more than 10% of the German population but lacks targeted therapies as well as precise prognostic tools. We have access to a unique biobank of human kidney biopsies with extensive clinical follow up data of up to 10 years. We are aiming to characterize 10 kidney diseases (common and rare) contributing to a majority of CKD by using single nuclear snRNA-seq and snATACseq from frozen human kidney biopsies. Living kidney donor biopsies will serve as healthy controls. We are aiming for 200 biopsies (20 per kidney disease type) with a minimum of 5 year follow up data. Among the 20 biopsies up to 10 will be included with stable kidney function during follow up and 10 with significant decline of kidney function. We will deploy new strategies for pooling patient samples and demultiplexing the data based on SNP analysis, as well as functional genomics methods tailored to single cell data to extract information about deregulated pathways, transcription factors and other processes, to identify novel biomarkers and therapeutic targets. This ambitious interdisciplinary project requires the expertise of nephrologists, computational and molecular biologists, and machine learners. It will only be possible by leveraging the resources at the DFG sequencing centers. The passion that drives this project results from a simple hypothesis: It is possible to find novel urgently needed therapeutic targets and prognostic measures for CKD using state of the art single cells genomics.