基于多重免疫荧光的Treg细胞自动化稳健归一化与情境感知拯救

CCR8⁺FoxP3⁺ regulatory T cells (Tregs) are a key immunosuppressive subset in solid tumors, but their accurate quantification in multiplex immunofluorescence (mIF) is hindered by slide-to-slide staining variability, dim FoxP3 signal, and background-driven CCR8 false positives. We developed a fully automated, background-aware R pipeline for robust cross-slide calling of CCR8⁺FoxP3⁺ cells from HALO single-cell data across NSCLC, CRC, GEJ, and HNSCC.Single-cell intensities for CCR8 and FoxP3 were transformed using inverse hyperbolic sine and normalized per slide using robust z-scores (rz) computed from medians and MADs, with fallbacks when FoxP3⁻ or CCR8⁻ reference populations were sparse. FoxP3 positivity was reassessed using a two-stage approach: (i) “soft” FoxP3 calls integrating HALO labels with high-rz outliers, and (ii) a strict rescue rule in which each cell's FoxP3 rz was evaluated against the empirical FoxP3⁻ distribution, requiring both a minimum rz floor and low FoxP3 background-z, preventing inflation of false positives. CCR8 calling used a spatially explicit background model: slides were partitioned into fine spatial bins, and CCR8 background medians and dispersions were estimated from local CCR8⁻ cells, with smoothing across sparse regions. This produced a background z-score (bg_z) and a background-adjusted CCR8 rz (rz_bg). From these, we defined a lenient background-aware CCR8 call and a strict CCR8 keeper enforcing strong evidence and artifact filters (cytoplasm completeness, CK negativity, nucleus quality). Final CCR8⁺FoxP3⁺ assignments used a FoxP3-aware rule: FoxP3⁺ cells were evaluated with lenient CCR8 criteria to recover dim true positives, whereas FoxP3⁻ cells required the strict CCR8 keeper to prevent spurious double-positive inflation. Agreement analyses showed that the strict CCR8 criteria substantially reduced Halo-only artifacts while adding very few “rescued-only” events. Conversely, FoxP3 soft-rescue recovered dim nuclei primarily in low-background regions. Processing was optimized using Arrow to avoid loading full datasets into memory, enabling analysis of >10 million cells on a standard workstation. The combined FoxP3 normalization, empirical rescue, and CCR8 background-aware gating produced stable distributions across indications and improved both sensitivity and specificity for CCR8⁺FoxP3⁺ Treg quantification.