Flow-cytometry-based protocols for human blood/marrow immunophenotyping with minimal sample perturbation

Abstract

This protocol provides instructions to improve flow cytometry analysis of marrow/peripheral blood cells by avoiding erythrolytic solutions, density gradients, and washing steps. We describe two basic approaches for identifying cell surface antigens with minimal sample perturbation, which have been successfully used to identify healthy and pathologically rare cells. The greatest advantage of these approaches is that they minimize the unwanted effect caused by sample preparation, allowing for improved study of live cells at the point of analysis. For complete details on the use and execution of this protocol, please refer to Petriz et al. (2018).

Keywords: Cancer; Clinical Protocol; Flow Cytometry/Mass Cytometry; Health Sciences; Immunology; Stem Cells.

Graphical abstract

Figure 1

Dual blue and violet laser side scatter using unlysed whole blood Erythrocytes, leukocytes, and platelets are separated on the basis of light scatter by plotting BSSC vs. VSSC. Hemoglobin absorption of light at 405 nm, reduces the erythrocyte 405 nm violet SSC signal, shifting the red blood cell population relative to leukocytes and platelets.

Figure 2

Scatter contribution of erythrocytes that are coincident with leukocytes The upper row shows the contribution of erythrocytes that are coincident with RBCs when the blue side scatter is used, whereas the lower row shows how the contribution of coincident RBCs is minimized when the violet side scatter is selected. Nucleated cells were gated from a plot of side scatter versus DyeCycle Violet (DCV) fluorescence density plots to visualize nucleated and non-nucleated cells.

Figure 3

Effect of red cell lysing reagents on cell loss and subsets Representative forward scatter (FSC) versus side scatter (SSC) density plots comparing the protocol used for minimal sample perturbation in (A) and the effect of lysing reagents in (B), showing alterations in the scatter measurements. Cell counts provided 1782.04 events/μL when using unlysed human peripheral blood (A) and 1049.08 events/μL after a 10 min ammonium chloride lysis protocol (B).

Figure 4

Gating strategy according to the target population of erythrocytes, platelets, and leukocytes For the study of erythrocytes, gate the 488 nm BSSC-H vs. 405 nm VSSC and logarithmic display excluding platelets, leukocytes and noise. For the study of platelets, gate the 488 nm BSSC-H vs. 405 nm VSSC and logarithmic display including leukocytes and excluding erythrocytes. Use this gate to create a platelet differential scatterplot now excluding leukocytes. For the study of leukocytes, gate the 488 nm BSSC-H vs. 405 nm VSSC and logarithmic display including platelets and excluding erythrocytes. Use this gate to create a leukocyte differential scatterplot now excluding platelets.

Figure 5

CD34+ progenitor counting in mobilized peripheral blood Threshold was set on FITC-CD45 fluorescence (A) to discriminate erythrocytes, platelets and debris. CD45+ events were subsequently gated on BSSC vs. VSSC (B), FSC vs. SSC (C), FITC-CD45 vs. PE-CD34 (D) and VSSC vs. PE-CD34 (E). Region (R) 2 comprehends CD34+ progenitor cells, which are further gated in VSCC vs. FSC (F) and selected in R3.

Figure 6

CD34+ progenitor counting in mobilized peripheral blood involving the use of DNA fluorescent stains Boolean gating strategy was performed following the ISHAGE guidelines for CD34 and CD45 staining. Region (R) 1 was set in a BSSC vs. DCV-H dot-plot (A) to include nucleated cell events and applied in a DCV-H vs. DCV-A dot-plot (B) for doublet discrimination. R2 including single cells was applied in a FSC-H vs. BSSC-H dot-plot (C) to gate leukocytes (R3). R3 was gated in a BSSC vs. FITC-CD45 dot-plot (D) to select CD45+ events in R4, which was applied to display CD34+ in a PE-CD34-H vs. BSSC-H dot plot (E) and in a PE-CD34-H vs. FITC-CD45-H dot plot (F).

Figure 7

CD34+ progenitor counting in mobilized peripheral blood by light scatter and fluorescent singlet discrimination Threshold was set on a DCV-H vs VSSC-H dot plot (A) and DCV+ events were subsequently gated on scatter (B), CD45 events (C), and CD34 events (D). CD45 and CD34 fluorescence was plotted to identify CD34 positive cells (E). Blue vs. Violet SSC was used to exclude coincident events (F). Violet SCC vs. CD45 fluorescence was used to select CD34+ events in R3 (G) and subsequently gated on light scatter (H), on light scatter singlets (I) and on fluorescent singlets (J).

Figure 8

Flow cytometry height (H) data is generally more accurate than area (A) data Forward scatter (FSC) versus side scatter (SSC) density plots indicating the high contribution in area from background coincidence or swarm detection from unlysed red blood cells when FSC-A vs. SSC-A are used to display leukocytes in (A). FSC-H vs. BSSC-H in (B) and FSC-H vs. VSSC-H in (C) displays accurate leukocyte scatter profiles when the Height parameter is used.

Figure 9

Effect of choosing an appropriate threshold for event triggering Unlysed whole blood cell data obtained with a fluorescence threshold enables analysis of low forward scatter. Reduced FSC is an indicator of cell death as a consequence of cell shrinkage, and loss of cell membrane integrity have impact in decreased refractive index of cells. In general, granulocytes are the most affected cells, whereas populations of apoptotic lymphocytes and monocytes can also be discerned, globally representing more than 25% of total events as displayed here.

Figure 10

Setting of threshold and voltage used to exclude non-nucleated cells Side scatter versus DyeCycle Violet (DCV) fluorescence density plots display inappropriate thresholding, low DCV fluorescence intensity, and poor signal to noise ratio (A). Note that the setting of threshold is higher in (B), allowing an optimal separation from non-nucleated cells and debris. The DCV voltage was also set such that all DCV+ events were captured on scale and adjusted to encompass the nucleated cell population and instrument background noise.

Figure 11

Spectral compensation for flow cytometry with minimal perturbation assays Nucleated cells were gated from a plot of side scatter versus DyeCycle Violet (DCV) fluorescence density plots to visualize non-transformed compensated data.

Figure 12

Spectral compensation for flow cytometry with minimal perturbation assays Nucleated cells were gated from a plot of side scatter versus DyeCycle Violet (DCV) fluorescence contour plots to visualize transformed compensated data.