Ensuring Full Spectrum Flow Cytometry Data Quality for High‐Dimensional Data Analysis

Abstract

Full spectrum flow cytometry (FSFC) allows for the analysis of more than 40 parameters at the single-cell level. Compared to the practice of manual gating, high-dimensional data analysis can be used to fully explore single-cell datasets and reduce analysis time. As panel size and complexity increases so too does the detail and time required to prepare and validate the quality of the resulting data for use in downstream high-dimensional data analyses. To ensure data analysis algorithms can be used efficiently and to avoid artifacts, some important steps should be considered. These include data cleaning (such as eliminating variable signal change over time, removing cell doublets, and antibody aggregates), proper unmixing of full spectrum data, ensuring correct scale transformation, and correcting for batch effects. We have developed a methodical step-by-step protocol to prepare full spectrum high-dimensional data for use with high-dimensional data analyses, with a focus on visualizing the impact of each step of data preparation using dimensionality reduction algorithms. Application of our workflow will aid FSFC users in their efforts to apply quality control methods to their datasets for use in high-dimensional analysis, and help them to obtain valid and reproducible results.

A 19‐color single‐tube full spectrum flow cytometry assay for the detection of measurable residual disease in acute myeloid leukemia

“…This cell mixture was subsequently used for individual stainings with each of the 19 antibodies, and unmixing was performed after setting the FSC/SSC gates on populations with the brightest marker expression (Supplementary Figure S3). This approach resulted in considerably improved unmixing results, and only required minor modifications of the compensation matrix in agreement with published protocols [22,23]. In general, these fixed settings were stable for up to 1 month before inaccuracies increased and required manual adjustments of more than 3% to the compensation matrix after sample acquisition.…”

Flow Cytometry: The Next Revolution

“…As already mentioned, the advent of spectral cytometry introduced complexities beyond the traditional polychromatic approach [31,206,207]. While simple matrix inversion has been used for inter-channel compensation (unmixing) in traditional cytometry [33,208], spectral cytometry requires more advanced methods [27].…”

OMIP‐097: High‐parameter phenotyping of human platelets by spectral flow cytometry

“…depending on the study design [40]. Automatic analysis by FAUST identifies and annotates four significant phenotypes, which account for 75% of platelets in the sample (Figure 1A).…”

“…A common “cleaning” step precedes analysis, where single platelets are gated on scatter and CD61 fluorescence (Figure 1A). Some datasets may benefit from additional preprocessing (e.g., normalization and subsampling) depending on the study design [40]. Automatic analysis by FAUST identifies and annotates four significant phenotypes, which account for ~75% of platelets in the sample (Figure 1A).…”