Journal: Frontiers in Immunology

Article Title: An optimized spectral flow cytometry pipeline for longitudinal blood leukocyte immunophenotyping reveals stable, individually distinct immune signatures

doi: 10.3389/fimmu.2026.1959895

Figure Lengend Snippet: Comparison of PaCMAP-based and traditional 2D biaxial gating consistency for leukocyte subset identification. CD-CHEX–stabilised blood from two donors (different lots; CD1 and CD2) was gated across up to four experimental timepoints by three independent expert operators using either traditional 2D hierarchical biaxial gating (purple) or PaCMAP dimensionality-reduction-based gating (green). Gating purity was assessed against UMAP-defined reference gates drawn by a single expert. (A) Longitudinal trajectories of correct-assignment percentages (% cells landing in the corresponding UMAP reference gate) across timepoints (Vo1 to Vo4), faceted by population (columns); the two donors are distinguished within each facet by point shape (circle, triangle) and mean-line style (solid, long-dashed). Each bold line links the per-donor mean at each timepoint (filled points, colored by method); dotted light lines are individual operator trajectories (largely overlapping and difficult to discern for PaCMAP). The y-axis is pseudo-log-scaled to resolve values near 100% (using scales::pseudo_log_trans(sigma = 0.1) in R). (B) Boxplots of correct-assignment percentages per population; boxes show the interquartile range (IQR), whiskers extend to 1.5 × IQR, white diamonds mark the group mean, and dots are individual operator × timepoint observations, colored by method and shaped by donor. (C) Boxplots of contamination percentages (% cells from a gated population landing in a non-corresponding UMAP reference gate) faceted by UMAP gated population, on a pseudo-log axis to accommodate the many zero values; boxes and dots as in (B) , with donor shown by point shape. (D) Summary of Wilcoxon rank-sum comparisons (Benjamini–Hochberg-adjusted) between methods across six metrics: correct-assignment temporal stability (SD), correct-assignment inter-rater ICC, mean contamination, proportion of zero-contamination events, contamination temporal stability (SD) and contamination inter-rater ICC; columns give each method’s group mean, the adjusted p-value and the direction; cell color indicates the significant direction (green, PaCMAP superior; orange, 2D superior; white, p ≥ 0.05). ICC (intraclass correlation coefficient) was computed with a two-way model, absolute agreement, single unit (irr::icc); groups with undefined between-operator variance were excluded as degenerate (fewer than two timepoints or three raters, or near-zero overall variance [SD < 0.01]; groups with identical values across raters were treated as perfect agreement), leaving n = 15 (PaCMAP) and n = 16 (2D) for correct assignment. Temporal-stability SD, mean contamination and the zero-contamination proportion were computed per operator and then compared between methods, isolating within-operator consistency; ICC uses the three operators as raters and therefore quantifies between-operator agreement.

Article Snippet: CD-CHEX Plus stabilized human blood (213323, lots 32680071, 41550071, 50690071 and 53210071, Streck) was used as a biological-invariant control.

Techniques: Comparison, Stability, Immunocytochemistry

Journal: Frontiers in Immunology

Article Title: An optimized spectral flow cytometry pipeline for longitudinal blood leukocyte immunophenotyping reveals stable, individually distinct immune signatures

doi: 10.3389/fimmu.2026.1959895

Figure Lengend Snippet: Pre-analytical handling effects on leukocyte composition and two-tier normalization. Whole blood from healthy donors was processed under varying pre-analytical conditions (n = 3 replicates per condition) and immunophenotyped by flow cytometry; populations are expressed as a percentage of total leukocytes. (A–D) Mean leukocyte subset composition (stacked bars: neutrophils, monocytes/macrophages/dendritic cells, eosinophils, B cells, T cells) per condition, with the five-population Bray–Curtis PERMANOVA result above each panel: (A) refrigerated storage at 4°C for 1, 2 or 3 days (Day 1 fresh reference; samples were pooled then split from donor 1, 4, and 6); (B) cryopreservation in CryoStor CS10 or DMSO for 15 days versus fresh (samples were split from donor 4); (C) chemical fixation analyzed at Day 1 or Day 5 versus unfixed Day 1 (samples were split from donor 4); (D) one-week-stored versus freshly prepared antibody cocktail (samples were split from donor 4). Companion data of numerical mean values in . (E–H) Two sequential leukocyte count normalization tiers were compared against raw counts for longitudinal stability. Tier 1 rescaled within-sample (to CD45 + % [%CD45], or to cells/mL by counting beads [bead] or volume [vol]); Tier 2 added a CD-CHEX-anchored cross-batch correction to remove run-to-run drift (schemes detailed in ): (E) Lineage populations of CD-CHEX, including eight major lineage populations (as described in ), were assessed for count stability over time (y-axis, Bray–Curtis dissimilarity to the first timepoint per tier, x-axis, acquisition month; lower and flatter = more stable). Each line is one normalization scheme (color) for one held-out CD-CHEX lot (line style and point shape distinguish the two lots, CHEX 3268 and CHEX 5069), with raw counts (raw) shown in every facet as a common reference; facets are the three Tier families (%CD45, bead, vol). The dashed line marks a dissimilarity of 0.1 for reference. (F) Posterior contrasts of CD-CHEX lineage count stability for each scheme versus raw counts (model below). (G) Longitudinal stability of lineage and subpopulation counts from fresh blood of 10 donors (populations as in ). X-axis, visit in chronological order; y-axis, mean Bray–Curtis dissimilarity to the first visit across donors, with the shaded ribbon the standard error across donors; each line is one scheme (color), raw counts repeated in each facet as reference; facets are level (lineages, subpopulations) × Tier family. (H) Corresponding posterior contrasts versus raw counts. In (F, H) , Bray–Curtis dissimilarity to the reference timepoint was modelled by Beta regression (brms) with normalization scheme as a fixed effect and a random intercept per population group (CD-CHEX lot in F; donor in H). Points are the posterior median difference from raw counts (ΔBray–Curtis) and bars the 80% (thick) or 95% (thin) credible interval (CI); negative values indicate greater stability than raw. A scheme was classed as more stable (green) or less stable (orange) than raw when its 95% CI excluded zero, and equivalent (gray) when it included zero. P denotes the posterior probability that a scheme has lower temporal dissimilarity than raw (the proportion of posterior draws with ΔBray–Curtis < 0), such that P = 1.00 indicates every draw favored greater stability. All models converged well (Rˆ ≤ 1.01, no divergent transitions); model convergence and fit diagnostics are given in .

Article Snippet: CD-CHEX Plus stabilized human blood (213323, lots 32680071, 41550071, 50690071 and 53210071, Streck) was used as a biological-invariant control.

Techniques: Flow Cytometry, Stability, Counting