Kenneth J. Livak

Across cancer, one of the most frequent examples of histologic transformation is the evolution of follicular lymphoma to an aggressive large cell lymphoma. Despite recent progress, the understanding of the molecular and cellular underpinnings of transformation remains incomplete. In this study, we dissect the interplay of tumor and microenvironment cell populations across transformation through a multimodal investigation of 95 follicular lymphoma and transformed follicular lymphoma (tFL) samples, including single-cell and bulk RNA sequencing alongside spatial transcriptomics and proteomics, and validate findings across independent follicular lymphoma–tFL pairs. Upon transformation, fibroblasts and GPNMB+ macrophages increase, whereas lymph node–organizing follicular dendritic and CCL21+ fibroblastic reticular cells (FRC) are lost, resulting in an altered spatial distribution of cytokines that affects T-cell infiltration and macrophage differentiation and function. Secreted stromal and macrophage signals are further evident through noninvasive plasma proteomics. Taken together, our data reveal the expansion of macrophages and fibroblasts as key features of transformation with potential diagnostic and therapeutic implications. Significance: Single-cell and spatial profiling of indolent and tFL samples reveals transformation-associated microenvironment alterations, including increased GPNMB+ macrophages and fibroblasts and decreased follicular dendritic and CCL21+ FRCs. The altered chemokine landscape of tFL is detectable through plasma proteomics, affording potential diagnostic and therapeutic opportunities.

Patient metadata for the DLBCL cohorts included in this study.

Cost-calculator for IN-DEPTH experimental planning.

GSVA pathways identified in DLBCL datasets at multicellular and single-cell resolution using SGCC.

Additional supporting data for single-cell SGCC analysis in the DLBCL study.

Additional validation and parameter optimization of the SGCC method, including simulation benchmarking against classical spatial statistical methods and robustness testing across parameter settings.

Spatial transcriptomics and proteomics have enabled profound insights into tissue organization, yet these technologies remain largely disparate, and emerging same-slide multiomics approaches are limited in plex, spatial resolution, signal retention, and integrative analytics. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined, resource-efficient, commercially compatible workflow using single-cell spatial proteomics-derived imaging to guide transcriptomic capture on the same slide without RNA signal loss. To integrate modalities beyond niche-level mapping, we developed Spectral Graph Cross-Correlation (SGCC), a proteomic–transcriptomic framework resolving spatially coordinated functional state changes across interacting cell populations. Applied to diffuse large B-cell lymphoma (DLBCL), IN-DEPTH and SGCC enabled stepwise discovery from Epstein–Barr virus (EBV)–positive and EBV-negative tumor comparisons with single-cell resolution, revealing coordinated tumor–macrophage–CD4 T-cell remodeling, immunosuppressive C1Q macrophage enrichment, CD4 T-cell dysfunction, and a candidate IL27–STAT3 signaling axis. Collectively, IN-DEPTH enables scalable spatial multiomics to uncover clinically relevant microenvironmental mechanisms and toward robust spatial multimodal AI models. Significance:IN-DEPTH enables same-slide spatial multiomics across commercial platforms via a protein-first strategy preserving protein epitopes, RNA quality, and tissue integrity. Coupled with SGCC, it resolves coordinated spatial immune remodeling, revealing EBV/LMP1-driven C1Q macrophage polarization and CD4 T-cell dysfunction in DLBCL, with broad applicability to other diseases.

GSVA pathways identified in the tonsil datasets using SGCC at multicellular level.

Additional supporting data for the ligand-receptor analysis.