In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (tubulointerstitium)

To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.

为了识别无法通过实验显微切割获取的细胞谱系特异性表达基因（cell-lineage-specific expression），我们开发了一种全基因组规模的迭代方法——虚拟纳米切割（in-silico nano-dissection），该方法采用机器学习框架，利用组织匀浆的高通量功能基因组学数据。本研究将虚拟纳米切割应用于慢性肾脏病（chronic kidney disease, CKD）研究场景，成功鉴定出足细胞（podocytes）特异性转录本；足细胞作为肾小球滤过器的关键细胞，与遗传性蛋白尿综合征及获得性慢性肾脏病密切相关。虚拟纳米切割的预测精度优于基于荧光标记小鼠足细胞的实验预测结果；本研究不仅鉴定出近期被证实与遗传性肾小球疾病相关的基因，还预测出与肾脏功能显著相关的基因。该虚拟纳米切割方法可广泛适用于多种生理功能与疾病场景下的细胞谱系特异性分析。我们针对人类肾脏活检组织匀浆的高通量基因表达数据搭建了机器学习框架，进而预测出全新的足细胞特异性基因。该预测结果已通过人类蛋白质图谱（Human Protein Atlas）在蛋白质层面得到验证，同时将预测精度与基于荧光标记小鼠足细胞的实验预测结果进行了对比。