Spatial multi-omics reveal distinct immunosuppressive lipid-laden macrophages in primary CNS lymphoma compared to systemic DLBCL
Abstract
Abstract Introduction Primary central nervous system lymphoma (PCNSL) is an aggressive B-cell lymphoma exhibiting unique central nervous system (CNS) tropism, and high recurrence rates despite sharing morphological and molecular features with systemic Diffuse Large B-Cell Lymphoma (DLBCL). Given the unique immune landscape of the CNS and the critical role of macrophages in neural tissue, we hypothesized that PCNSL may be sustained by a CNS-specific macrophage program distinct from DLBCL, representing a targetable mechanism underlying immune evasion, CNS confinement, and treatment resistance. While tumor-associated macrophages (TAMs), particularly CD163+ M2-like macrophages, are known to be enriched in PCNSL tumor microenvironment (TME), prior spatial studies have focused predominantly on tumor-intrinsic features and T-cell dysfunction, leaving macrophage organization and functional programming poorly characterized. Methods We employed cutting-edge spatial multi-omics using four complementary platforms to comprehensively profile macrophage heterogeneity. Formalin-fixed paraffin-embedded (FFPE) samples in TMA format (26 PCNSL, 89 DLBCL) were analyzed using a Xenium 380-gene immuno-oncology panel. GeoMx digital spatial profiling whole transcriptome analysis (DSP-WTA) was performed in 82 cases (17 PCNSL, 65 DLBCL) using CD3, CD20, and CD68 cell masks. Macrophage signatures identified from DSP were validated across independent single-cell RNA sequencing (scRNA-seq) datasets (PCNSL N=28, DLBCL N=17, reactive lymph nodes N=2), distinguishing microglia from monocyte-derived macrophages. CellScape high-plex imaging was used to confirm phenotypes at the protein level and assess spatial proximity and interactions in 24 PCNSL and 5 tonsil samples using 40 architecture, immune and macrophage markers. Results Xenium spatial profiling revealed significantly higher macrophage abundance in PCNSL versus DLBCL, confirmed by DSP-WTA (p=0.0002). DSP further demonstrated that PCNSL TAMs upregulate immunosuppressive genes (CRYAB, SPP1) while downregulating T-cell recruitment genes (CCL19, IGSF6), with enrichment of glycolysis, cholesterol homeostasis, and peroxisome pathways. The CXCL9:SPP1 expression ratio, a validated macrophage polarity biomarker of prognosis in cancer, was significantly reduced in PCNSL macrophages across both DSP and scRNA-seq datasets (p=0.027). Of specific interest, differentially expressed gene (DEG) projection and BayesPrism deconvolution of CD68+ regions of interest (ROIs) revealed enrichment of TREM2+ macrophages (p=0.00066) and elevated GPNMB+ lipid-laden macrophage (LLM) signatures (p=0.004) in PCNSL. scRNA-seq confirmed higher LLM signatures in PCNSL versus DLBCL (p=0.0052) and lymph nodes (p=1.2e-06), identifying a monocyte-derived subset enriched in cholesterol metabolism and high-density lipoprotein (HDL) binding pathways. Cell-cell communication analysis revealed enhanced interactions between LLMs and regulatory T-cells via secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and intercellular adhesion molecule 1 (ICAM1) signaling axes. CellScape imaging confirmed the presence of GPNMB+CD163+CD274+ macrophages in PCNSL, with spatial analysis showing that LLM-T cell proximity correlated with treatment response. Conclusion We define a CNS-specific macrophage program characterized by TREM2+ GPNMB+ lipid-laden macrophages that may foster CNS tropism in PNCSL, along with immune evasion through metabolic reprogramming and regulatory T-cell activation. This first comprehensive spatial multi-omics characterization of macrophage heterogeneity distinguishing PCNSL from DLBCL identifies TREM2, SPP1 and lipid metabolism as potential therapeutic targets for macrophage-directed immunotherapy in this disease of unmet clinical need.
Article Details
Authors (25)
Liang Hong
Centre for Clean Energy Technology, Faculty of Science
Min Liu
Shruti Sridhar
Charmaine Ong
1National University of Singapore, Cancer Science Institute of Singapore, Singapore, Singapore
Brooks Jennifer
3Bruker Spatial Biology, St Louis, United States
Florian Hamberger
3Bruker Spatial Biology, St Louis, United States
Brian Lane
3Bruker Spatial Biology, St Louis, United States
Daniel Sanchez
3Bruker Spatial Biology, St Louis, United States
Ranga Sudharshan
4University of Michigan Medical School, Department of Computational Medcine and Bioinformatics, Ann Arbor, United States
Ashley Tsang
4University of Michigan Medical School, Department of Computational Medcine and Bioinformatics, Ann Arbor, United States
Yanfen Peng
1National University of Singapore, Cancer Science Institute of Singapore, Singapore, Singapore
Chartsiam Tipgomut
1Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
Patrick Jaynes
1National University of Singapore, Cancer Science Institute of Singapore, Singapore, Singapore
Oliver Braubach
3Bruker Spatial Biology, St Louis, United States
Evan Keller
5University of Michigan, Department of Urology, Ann Arbor, United States
Arvind Rao
Michigan Center for Translational Pathology, University of Michigan
Sanjay De Mel
6National University Health System, Department of Haematology-Oncology, Singapore, Singapore
Joanne Lee
1National University Cancer Institute Singapore, Hematology-Oncology, Singapore, Singapore
Jayalakshmi S.
6National University Health System, Department of Haematology-Oncology, Singapore, Singapore
Qiang Pan-Hammarström
Division of Immunology, Department of Medical Biochemistry and Biophysics, Karolinska Institute
Char Tan
9Yong Loo Lin School of Medicine, National University of Singapore, Department of Pathology, Singapore, Singapore
Susan Swee Shan
9Yong Loo Lin School of Medicine, National University of Singapore, Department of Pathology, Singapore, Singapore
Siok Ng
7Yong Loo Lin School of Medicine, National University of Singapore, NUS Centre for Cancer Research, Singapore, Singapore
Claudio Tripodo
Anand Devaprasath Jeyasekharan