Ensuring Full Spectrum Flow Cytometry Data Quality for High-Dimensional Data Analysis.

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. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Data cleaning Basic Protocol 2: Validating the quality of unmixing Basic Protocol 3: Data scaling Basic Protocol 4: Batch-to-batch normalization.

Full spectrum flow cytometry and mass cytometry: A 32-marker panel comparison.

High-dimensional single-cell data has become an important tool in unraveling the complexity of the immune system and its involvement in homeostasis and a large array of pathologies. As technological tools are developed, researchers are adopting them to answer increasingly complex biological questions. Up until recently, mass cytometry (MC) has been the main technology employed in cytometric assays requiring more than 29 markers. Recently, however, with the introduction of full spectrum flow cytometry (FSFC), it has become possible to break the fluorescence barrier and go beyond 29 fluorescent parameters. In this study, in collaboration with the Stanford Human Immune Monitoring Center (HIMC), we compared five patient samples using an established immune panel developed by the HIMC using their MC platform. Using split samples and the same antibody panel, we were able to demonstrate highly comparable results between the two technologies using multiple data analysis approaches. We report here a direct comparison of two technology platforms (MC and FSFC) using a 32-marker flow cytometric immune monitoring panel that can identify all the previously described and anticipated immune subpopulations defined by this panel.

Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning.

Quantitative or qualitative differences in immunity may drive clinical severity in COVID-19. Although longitudinal studies to record the course of immunological changes are ample, they do not necessarily predict clinical progression at the time of hospital admission. Here we show, by a machine learning approach using serum pro-inflammatory, anti-inflammatory and anti-viral cytokine and anti-SARS-CoV-2 antibody measurements as input data, that COVID-19 patients cluster into three distinct immune phenotype groups. These immune-types, determined by unsupervised hierarchical clustering that is agnostic to severity, predict clinical course. The identified immune-types do not associate with disease duration at hospital admittance, but rather reflect variations in the nature and kinetics of individual patient's immune response. Thus, our work provides an immune-type based scheme to stratify COVID-19 patients at hospital admittance into high and low risk clinical categories with distinct cytokine and antibody profiles that may guide personalized therapy.

Normal bone marrow signal-transduction profiles: a requisite for enhanced detection of signaling dysregulations in AML.

Molecular and cytogenetic alterations are involved in virtually every facet of acute myeloid leukemia (AML), including dysregulation of major signal-transduction pathways. The present study examines 5 phosphoproteins (pErk, pAkt, pS6, pStat3, and pStat5) in response to 5 cytokine/growth factors (stem cell factor [SCF], Flt-3/Flk-2 ligand [FL], granulocyte/macrophage-colony stimulating factor [GM-CSF], interleukin-3 [IL-3], and granulocyte-CSF [G-CSF]) within 7 immunophenotypically defined populations, spanning progenitor to mature myeloid/myelomonocytic cells in normal bone marrows with further comparison to AML samples. The normal cohort showed pathway-specific responses related to lineage, maturation, and stimulus. Heterogeneous-signaling responses were seen in homogeneous immunophenotypic subsets emphasizing the additive information of signaling. These profiles provided a critical baseline for detection of dysregulated signaling in AML falling into 4 broad categories, viz lack of response, increased activation, altered constitutive expression, and dysregulated response kinetics, easily identified in 10 of 12 AMLs. These studies clearly show robust and reproducible flow cytometry phosphoprotein analyses capable of detecting abnormal signal-transduction responses in AML potentially contributing to definitive reliable identification of abnormal cells. As functional correlates of underlying genetic abnormalities, signal-transduction abnormalities may provide more stable indicators of abnormal cells than immunophenotyping which frequently changes after therapy and disease recurrence.