Primary Sjögren’s disease (SjD) is an autoimmune disease with inflammation of the salivary and lacrimal glands and a 15-fold increased risk of lymphoma. The most common subtype is non-Hodgkin lymphoma of the B cell type in the mucosa associated lymphoid tissue (MALT). MALT lymphomas mostly arise in the major salivary glands, the site for inflammation in SjD. The molecular mechanisms behind transition from benign B cell activation to malignant B cell clones and lymphoma are not fully understood. Clinical risk factors have been identified, but improved biomarkers are needed. There is hitherto no study investigating the molecular mechanisms in PBMC from SjD patients with incident lymphomas before treatment, using technologies with single-cell resolution. The Autolymphoma cohort consists of SjD with lymphomas sampled at time of lymphoma diagnosis (time point 0) and at 6, 12, and 24 months with DNA, RNA, PBMC, serum and plasma, and extensive clinical data. In our previous study using scRNA-seq and scVDJ-seq of purified B cells, we found that SSA/SSB antibody positive SjD presented skewed B cell subpopulations, reduced Immunoglobulin class switch, specific VDJ gene usage and decreased VDJ mutations (Arvidsson G., et al., 2024). We have now performed single-cell sequencing analyses on PBMCs from a selected subset of the SjD Autolymphoma patients and controls. These new datasets include single-cell RNA-seq (10x Genomics including scVDJ-seq) and spatial proteomics profiling of 80 cell-surface proteins (Molecular Pixelation; Pixelgen Technologies)- Questions to investigate: • Is there a specific B- or T-cell subpopulation overrepresented in SjD with lymphoma that can be used as a biomarker of lymphoma? • Can knowledge of specific B- or T-cell subpopulations and certain BCR or TCR gene usage overrepresented in SjD with lymphoma, increase our understanding of the mechanisms behind lymphoma development? • Are there changes to the spatial organization of the cell-surface proteome in PBMCs from SjD with lymphoma? Methodologies: Two complementary methods have been employed to generate high- resolution molecular data from these samples: 1. Single-Cell RNA Sequencing (scRNA-seq): o Data include 5’ gene expression profiles and matched BCR and TCR sequencing libraries generated using the 10x Genomics platform. o Approximately 10,000 cells were analyzed per sample, resulting in sequencing data for a total of 160,000 cells across all 16 samples. 2. Molecular Pixelation (MPX), Single Cell Spatial Proteomics: o This dataset provides cell-surface protein expression profiles, including spatial polarity and co-localization information for 80 targeted immunology-related proteins. o Data were generated using the MPX version 2 kit from Pixelgen, analyzing 1,000 cells per sample, yielding spatial proteomics data for a total of 16,000 cells.