traditional manual gating strategies are often time-intensive, place a high burden on the analyzer, and are susceptible to bias between analyzers. Several automated gating methods have shown to exceed performance of manual gating for a limited number of cell subsets. However, many of the automated algorithms still require significant manual interventions or have yet to demonstrate their utility in large datasets. therefore, we developed an approach that utilizes a previously published automated algorithm (opencyto framework) with a manually created hierarchically cell gating template implemented, along with a custom developed visualization software (flowAnnotator) to rapidly and efficiently analyze immunophenotyping data in large population studies. This approach allows pre-defining populations that can be analyzed solely by automated analysis and incorporating manual refinement for smaller downstream populations. We validated this method with traditional manual gating strategies for 24 subsets of T cells, B cells, NK cells, monocytes and dendritic cells in 931 participants from the Health and Retirement Study (HRS). our results show a high degree of correlation (r ≥ 0.80) for 18 (78%) of the 24 cell subsets. For the remaining subsets, the correlation was low (<0.80) primarily because of the low numbers of events recorded in these subsets. The mean difference in the absolute counts between the hybrid method and manual gating strategy of these cell subsets showed results that were very similar to the traditional manual gating method. We describe a practical method for standardization of immunophenotyping methods in large scale population studies that provides a rapid, accurate and reproducible alternative to labor intensive manual gating strategies. Flow cytometry (FCM) provides a high dimensional quantitative measure for single cell analysis of the immune system. Manual gating using analyzer-defined boundaries or "gates" to identify cell populations of interest is commonly analyzed using proprietary software (e.g. FlowJo Tree Star Inc version 10). In the context of large-scale epidemiological investigations involving thousands of samples, this time consuming and labor-intensive process depends on the skill of the analyst, thereby introducing subjectivity that can increase variability among analysts and limit reproducibility of flow cytometry assays. Recent technological advancements in computational methods help reduce the subjectivity intrinsic to manual gating for multi-dimensional flow cytometry data and promote standardization of immunophenotyping analysis in large population studies. Several automated analyses tools such as OpenCyto 1 and FLOCK 2,3 and hybrid tools such as DAFi 4 and FlowGM 5 have been validated in recent years with good concordance when compared to manually gated datasets. Additional tools such as CytoML 6 allow data to be shared across platforms, which makes a streamlined analysis using both automated and manual analysis possible. Recently, the Flow Cytometry
We described a targeted mass spectrometry assay based on selected reaction monitoring (SRM) for five apolipoproteins (apoA1, apoB, apoJ, apoD, and apoE) in plasma lipoproteins isolated by anion exchange fast protein liquid chromatography using only 100 µL of plasma. We performed analytical characterization of the SRM assay and evaluated reproducibility of the entire workflow. The limit of detections for apoA1, apoB, apoD, apoJ, and apoE were 0.6, 4.6, 0.8, 1.2, and 0.7 nM, respectively; the limit of quantifications was 8.3 nM for all peptides except apoD (4.2 nM). The SRM assay was linear from 0.4 to 1667 nM. The intra-day and inter-day and total repeatability (CV%) of the assay ranged from 2.2% to 21.7% for all five peptides. The intra-day and inter-day and total reproducibility of the entire workflow ranged from 12.2% to 43.9% for all five peptides in fractionated high-density lipoprotein, low-density lipoprotein, and IDL. In the future, we will apply this workflow to investigate the association of fractionated plasma lipoproteins with amyloid deposition and cognitive changes in the context of Alzheimer's disease.
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