How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

Braanker, Hannah den; Bongenaar, Margot; Lubberts, Erik

doi:10.3389/fimmu.2021.768113

Cited by 22 publications

(15 citation statements)

References 33 publications

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“…Other authors (den Braanker, Bongenaar, & Lubberts, 2021; Liechti et al., 2021) have aimed to describe the requirements for FSFC data for high‐dimensional analysis. To build on this existing literature, we believe there is a need for a detailed and guided step‐by‐step protocol to follow the recommended steps.…”

Section: Introductionmentioning

confidence: 99%

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

Ferrer‐Font

Kraker²,

Hally

et al. 2023

Current Protocols

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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.

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Section: Introductionmentioning

confidence: 99%

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

Ferrer‐Font

Kraker²,

Hally

et al. 2023

Current Protocols

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show abstract

“…45,46 High-dimensional evaluation of large datasets by flow cytometry has previously been hampered by technical variation between experiments, an issue termed 'batch effects'. 47,48 Previous studies assessing batch effects have typically focused on shorter time periods, fewer batches or utilised CyTOF, 41,[49][50][51] making this study notable for its exploration of longer term batch effects as they apply to spectral flow cytometry. The robustness of our protocol for longitudinal immunophenotyping was demonstrated by stable staining performance over the 3-month time interval during which batches were run (Figure 3).…”

Section: Discussionmentioning

confidence: 99%

Design, optimisation and standardisation of a high‐dimensional spectral flow cytometry workflow assessing T‐cell immunophenotype in patients with melanoma

Edwards,

Andrews,

Burridge

et al. 2023

Clin & Trans Imm

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ObjectivesDespite the success of immune checkpoint blockade, most metastatic melanoma patients fail to respond to therapy or experience severe toxicity. Assessment of biomarkers and immunophenotypes before or early into treatment will help to understand favourable responses and improve therapeutic outcomes.MethodsWe present a high‐dimensional approach for blood T‐cell profiling using three multi‐parameter cytometry panels: (1) a TruCount panel for absolute cell counts, (2) a 27‐colour spectral panel assessing T‐cell markers and (3) a 20‐colour spectral panel evaluating intracellular cytokine expression. Pre‐treatment blood mononuclear cells from patients and healthy controls were cryopreserved before staining across 11 batches. Batch effects were tracked using a single‐donor control and the suitability of normalisation was assessed. The data were analysed using manual gating and high‐dimensional strategies.ResultsBatch‐to‐batch variation was minimal, as demonstrated by the dimensionality reduction of batch‐control samples, and normalisation did not improve manual or high‐dimensional analysis. Application of the workflow demonstrated the capacity of the panels and showed that patients had fewer lymphocytes than controls (P = 0.0027), due to lower naive CD4+ (P = 0.015) and CD8+ (P = 0.011) T cells and follicular helper T cells (P = 0.00076). Patients showed trends for higher proportions of Ki67 and IL‐2‐expressing cells within CD4+ and CD8+ memory subsets, and increased CD57 and EOMES expression within TCRγδ+ T cells.ConclusionOur optimised high‐parameter spectral cytometry approach provided in‐depth profiling of blood T cells and found differences in patient immunophenotype at baseline. The robustness of our workflow, as demonstrated by minimal batch effects, makes this approach highly suitable for the longitudinal evaluation of immunotherapy effects.

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“…Manual gating was performed on cellular events > singlets > live cells (Zombie NIR − ) > lineage negative cells (CD3 − , CD4 − , CD5 − , CD11b − , CD8 − , CD45R − , Ly76/TER-119 − ), for which FCS files were exported. Data transformation, quality control, and dimensionality reduction was a performed essentially as described (den Braanker et al, 2021). Briefly, flow cytometry data was imported to R using the flowCore package (Hahne et al, 2009) and was normalized using the arcsinh cofactor transformation method of the flowVS package (Azad et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

Single cell proteomics characterization of bone marrow hematopoiesis with distinct Ras pathway lesions

Karra,

Finger,

Shechtman

et al. 2023

Preprint

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Normal hematopoiesis requires constant prolific production of different blood cell lineages by multipotent hematopoietic stem cells (HSC). Stem- and progenitor- cells need to balance dormancy with proliferation. How genetic alterations impact frequency, lineage potential, and metabolism of HSC is largely unknown. Here, we compared induced expression of KRASG12Dor RasGRP1 to normal hematopoiesis. At low-resolution, both Ras pathway lesions result in skewing towards myeloid lineages. Single-cell resolution CyTOF proteomics unmasked an expansion of HSC- and progenitor- compartments for RasGRP1, contrasted by a depletion for KRASG12D. SCENITH™ quantitates protein synthesis with single-cell precision and corroborated that immature cells display low metabolic SCENITH™ rates. Both RasGRP1 and KRASG12Delevated mean SCENITH™ signals in immature cells. However, RasGRP1-overexpressing stem cells retain a metabolically quiescent cell-fraction, whereas this fraction diminishes for KRASG12D. Our temporal single cell proteomics and metabolomics datasets provide a resource of mechanistic insights into altered hematopoiesis at single cell resolution.

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How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

Cited by 22 publications

References 33 publications

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

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

Design, optimisation and standardisation of a high‐dimensional spectral flow cytometry workflow assessing T‐cell immunophenotype in patients with melanoma

Single cell proteomics characterization of bone marrow hematopoiesis with distinct Ras pathway lesions

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