Fread Kristen I, Strickland William D, Nolan Garry P, Zunder Eli R
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA,
Pac Symp Biocomput. 2017;22:588-598. doi: 10.1142/9789813207813_0054.
Pooled sample analysis by mass cytometry barcoding carries many advantages: reduced antibody consumption, increased sample throughput, removal of cell doublets, reduction of cross-contamination by sample carryover, and the elimination of tube-to-tube-variability in antibody staining. A single-cell debarcoding algorithm was previously developed to improve the accuracy and yield of sample deconvolution, but this method was limited to using fixed parameters for debarcoding stringency filtering, which could introduce cell-specific or sample-specific bias to cell yield in scenarios where barcode staining intensity and variance are not uniform across the pooled samples. To address this issue, we have updated the algorithm to output debarcoding parameters for every cell in the sample-assigned FCS files, which allows for visualization and analysis of these parameters via flow cytometry analysis software. This strategy can be used to detect cell type-specific and sample-specific effects on the underlying cell data that arise during the debarcoding process. An additional benefit to this strategy is the decoupling of barcode stringency filtering from the debarcoding and sample assignment process. This is accomplished by removing the stringency filters during sample assignment, and then filtering after the fact with 1- and 2-dimensional gating on the debarcoding parameters which are output with the FCS files. These data exploration strategies serve as an important quality check for barcoded mass cytometry datasets, and allow cell type and sample-specific stringency adjustment that can remove bias in cell yield introduced during the debarcoding process.
减少抗体消耗、提高样本通量、去除细胞 doublets、减少样本残留导致的交叉污染以及消除抗体染色中管间差异。先前开发了一种单细胞去条形码算法以提高样本解卷积的准确性和产量,但该方法仅限于使用固定参数进行条形码严格性过滤,在条形码染色强度和方差在混合样本中不均匀的情况下,这可能会给细胞产量引入细胞特异性或样本特异性偏差。为了解决这个问题,我们更新了算法,以便在分配样本的FCS文件中为每个细胞输出去条形码参数,这允许通过流式细胞术分析软件对这些参数进行可视化和分析。这种策略可用于检测在去条形码过程中出现的对基础细胞数据的细胞类型特异性和样本特异性影响。该策略的另一个好处是条形码严格性过滤与去条形码和样本分配过程的解耦分离。这是通过在样本分配期间去除严格性过滤器,然后在事后根据与FCS文件一起输出的去条形码参数进行一维和二维门控过滤来实现的。这些数据探索策略是条形码质谱流式细胞术数据集的重要质量检查,并允许进行细胞类型和样本特异性严格性调整,从而消除去条形码过程中引入的细胞产量偏差。