Vitrinel Burcu, Iannitelli Dylan E, Mazzoni Esteban O, Christiaen Lionel, Vogel Christine
Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York 10003, United States.
Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, United States.
ACS Omega. 2020 Jun 19;5(25):15537-15546. doi: 10.1021/acsomega.0c01191. eCollection 2020 Jun 30.
The rise of single-cell transcriptomics has created an urgent need for similar approaches that use a minimal number of cells to quantify expression levels of proteins. We integrated and optimized multiple recent developments to establish a proteomics workflow to quantify proteins from as few as 1000 mammalian stem cells. The method uses chemical peptide labeling, does not require specific equipment other than cell lysis tools, and quantifies >2500 proteins with high reproducibility. We validated the method by comparing mouse embryonic stem cells and in vitro differentiated motor neurons. We identify differentially expressed proteins with small fold changes and a dynamic range in abundance similar to that of standard methods. Protein abundance measurements obtained with our protocol compared well to corresponding transcript abundance and to measurements using standard inputs. The protocol is also applicable to other systems, such as fluorescence-activated cell sorting (FACS)-purified cells from the tunicate . Therefore, we offer a straightforward and accurate method to acquire proteomics data from minimal input samples.
单细胞转录组学的兴起迫切需要类似的方法,即使用最少数量的细胞来定量蛋白质的表达水平。我们整合并优化了多项最新进展,建立了一种蛋白质组学工作流程,以从低至1000个哺乳动物干细胞中定量蛋白质。该方法采用化学肽标记,除细胞裂解工具外无需特定设备,可高度可重复地定量超过2500种蛋白质。我们通过比较小鼠胚胎干细胞和体外分化的运动神经元验证了该方法。我们识别出具有小倍数变化且丰度动态范围与标准方法相似的差异表达蛋白质。用我们的方案获得的蛋白质丰度测量值与相应的转录本丰度以及使用标准输入的测量值相比效果良好。该方案也适用于其他系统,例如来自被囊动物的荧光激活细胞分选(FACS)纯化细胞。因此,我们提供了一种直接且准确的方法,可从极少输入样本中获取蛋白质组学数据。