Spatially resolved gene expression profiles are key to understand tissue organization and function. However, spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). Simulating varying reference quantities and qualities, we confirmed high prediction accuracy also with shallowly sequenced or small-sized scRNA-seq reference datasets. SPOTlight deconvolution of the mouse brain correctly mapped subtle neuronal cell states of the cortical layers and the defined architecture of the hippocampus. In human pancreatic cancer, we successfully segmented patient sections and further fine-mapped normal and neoplastic cell states. Trained on an external single-cell pancreatic tumor references, we further charted the localization of clinical-relevant and tumor-specific immune cell states, an illustrative example of its flexible application spectrum and future potential in digital pathology.

ObjectiveTo assess whether statin treatment is associated with a reduction in atherosclerotic cardiovascular disease (CVD) and mortality in old and very old adults with and without diabetes.DesignRetrospective cohort study.SettingDatabase of the Catalan primary care system (SIDIAP), Spain, 2006-15.Participants46 864 people aged 75 years or more without clinically recognised atherosclerotic CVD. Participants were stratified by presence of type 2 diabetes mellitus and as statin non-users or new users.Main outcome measuresIncidences of atherosclerotic CVD and all cause mortality compared using Cox proportional hazards modelling, adjusted by the propensity score of statin treatment. The relation of age with the effect of statins was assessed using both a categorical approach, stratifying the analysis by old (75-84 years) and very old (≥85 years) age groups, and a continuous analysis, using an additive Cox proportional hazard model.ResultsThe cohort included 46 864 participants (mean age 77 years; 63% women; median follow-up 5.6 years). In participants without diabetes, the hazard ratios for statin use in 75-84 year olds were 0.94 (95% confidence interval 0.86 to 1.04) for atherosclerotic CVD and 0.98 (0.91 to 1.05) for all cause mortality, and in those aged 85 and older were 0.93 (0.82 to 1.06) and 0.97 (0.90 to 1.05), respectively. In participants with diabetes, the hazard ratio of statin use in 75-84 year olds was 0.76 (0.65 to 0.89) for atherosclerotic CVD and 0.84 (0.75 to 0.94) for all cause mortality, and in those aged 85 and older were 0.82 (0.53 to 1.26) and 1.05 (0.86 to 1.28), respectively. Similarly, effect analysis of age in a continuous scale, using splines, corroborated the lack of beneficial statins effect for atherosclerotic CVD and all cause mortality in participants without diabetes older than 74 years. In participants with diabetes, statins showed a protective effect against atherosclerotic CVD and all cause mortality; this effect was substantially reduced beyond the age of 85 years and disappeared in nonagenarians.ConclusionsIn participants older than 74 years without type 2 diabetes, statin treatment was not associated with a reduction in atherosclerotic CVD or in all cause mortality, even when the incidence of atherosclerotic CVD was statistically significantly higher than the risk thresholds proposed for statin use. In the presence of diabetes, statin use was statistically significantly associated with reductions in the incidence of atherosclerotic CVD and in all cause mortality. This effect decreased after age 85 years and disappeared in nonagenarians.

Fatty acid (FA) uptake and altered metabolism constitute hallmarks of metastasis 1-2 yet it is unclear the biology behind it, or whether all dietary FAs are prometastatic. Here we show that dietary palmitic acid (PA), but not oleic acid or linoleic acid, promotes metastasis in oral carcinomas and melanoma. Unexpectedly, tumours from mice fed a short-term palm oil (PA)-rich diet, or tumour cells briefly exposed to PA in vitro, remain highly metastatic even when serially transplanted (without further exposure to high levels of PA). This PAinduced prometastatic memory requires the fatty acid transporter CD36 and is associated with the stable deposition of histone H3 lysine 4 trimethylation by the methyltransferase Set1A/COMPASS. Bulk, single-cell and positional RNA sequencing indicate that genes with this prometastatic memory predominantly relate to a neural signature that stimulates intratumour Schwann cells and innervation, two parameters strongly correlated with metastasis but etiologically poorly understood 3-4 . Mechanistically, tumour-associated Schwann cells secrete a specialized pro-regenerative extracellular matrix, whose ablation inhibits metastasis initiation. Both the PA-induced memory of this proneural signature and its long-term boost in metastasis require the transcription factor EGR2 and the glial cellstimulating peptide galanin. In sum, we provide evidence that a dietary metabolite provokes stable transcriptional and chromatin changes that lead to a long-term stimulation of metastasis, and that this is related to a pro-regenerative state of tumour-activated Schwann cells.

The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients, and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization. We generated a single-cell tumor immune atlas, jointly analyzing published data sets of >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions. Projecting immune cells from external tumors onto the atlas facilitated an automated cell annotation system. To enable in situ mapping of immune populations for digital pathology, we applied SPOTlight, combining single-cell and spatial transcriptomics data and identifying colocalization patterns of immune, stromal, and cancer cells in tumor sections. We expect the tumor immune cell atlas, together with our versatile toolbox for precision oncology, to advance currently applied stratification approaches for prognosis and immunotherapy.

No abstract