Rank-Preserving Alignment Enables Cross-platform Learning and Phenotyping for Single-Cell and Spatial Proteomics

Abstract

Decades of antibody-based protein profiling have generated valuable cohorts across evolving cytometry, sequencing and spatial imaging platforms. Differences in marker coverage, signal scale and antibody performance hinder joint analysis and reuse of cohorts with long-term clinical follow-up. Widely used RNA-based integration methods prioritize shared embeddings rather than directly comparable protein measurements. To fill in the gap, we developed RAMP (Rank-preserving Alignment of Multi-platform Proteomics), an unsupervised framework that aligns shared proteins without cell-type annotations. It combines sample-specific anchoring and cross-platform cell matching with bounded monotone transformations. The corrected values retain each protein marker's expression order across cells within a sample. Across seven integration tasks, RAMP achieved the strongest overall balance of batch correction and cell-type preservation. RAMP also improved marker-threshold transfer while preventing expression-order reversals observed with some comparison methods. These reversals made cell types with originally high marker expression appear lower-expressing than other populations. Such overcorrection can mislead cell identification and protein comparisons, even when the integrated embedding shows improved dataset mixing. By preserving each marker's expression order within a sample, RAMP protects the relationships needed to interpret normalized protein measurements. To investigate whether better harmonized inputs improve downstream learning, we used linear, nearest-neighbor and small neural-network models as a controlled, small-scale testbed. This approach tests an input quality question relevant to proteomic foundation models without undertaking large-scale pretraining. Under matched supervision, RAMP improved cross-platform annotation across all three model families and improved missing-marker prediction over unaligned inputs. These gains demonstrate the value of harmonized inputs for predictive learning and motivate evaluating RAMP as preprocessing for proteomic foundation models. In bone marrow, we used high-quality single-cell protein measurements to impute the failed CODEX CD19 channel, recovering B-cell spatial distributions. The completed panel improved B-cell separation from natural killer cells in the joint embedding. In melanoma, a proliferating CD8 T-cell state near tumor cells was associated with longer survival. Transfer such a spatial information to CITE-seq connected tumor proliferation and infiltration state to an adhesion and activation program involving PD-1, LFA-1 and CD2. In lung cancer, we used single-cell CyTOF measurements to predict unmeasured HLA-ABC states in vascular cancer-associated fibroblasts profiled by spatial imaging mass cytometry (IMC). Higher predicted scores were associated with lower immune-cell fractions and worse disease-free survival in the IMC cohort. In head and neck cancer, transferring a fibroblast/macrophage spatial niche score to CITE-seq identified a CD39/TIGIT-associated CD8 T-cell phenotype. Protein and RNA profiles supported checkpoint-associated features, and transfer back to CODEX showed correspondence with measured LAG3. RAMP enables existing single-cell and spatial cohorts to support new discoveries by connecting molecular programs, tissue organization and clinical outcomes across platforms.