Despite the many approaches to study differential splicing from RNA-seq, many challenges remain unsolved, including computing capacity and sequencing depth requirements. Here we present SUPPA2, a new method that addresses these challenges, and enables streamlined analysis across multiple conditions taking into account biological variability. Using experimental and simulated data, we show that SUPPA2 achieves higher accuracy compared to other methods, especially at low sequencing depth and short read length. We use SUPPA2 to identify novel Transformer2-regulated exons, novel microexons induced during differentiation of bipolar neurons, and novel intron retention events during erythroblast differentiation.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1417-1) contains supplementary material, which is available to authorized users.

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.

Brain metastases are the most common tumor of the brain with a dismal prognosis. A fraction of patients with brain metastasis benefit from treatment with immune checkpoint inhibitors (ICI) and the degree and phenotype of the immune cell infiltration has been used to predict response to ICI. However, the anatomical location of brain lesions limits access to tumor material to characterize the immune phenotype. Here, we characterize immune cells present in brain lesions and matched cerebrospinal fluid (CSF) using single-cell RNA sequencing combined with T cell receptor genotyping. Tumor immune infiltration and specifically CD8+ T cell infiltration can be discerned through the analysis of the CSF. Consistently, identical T cell receptor clonotypes are detected in brain lesions and CSF, confirming cell exchange between these compartments. The analysis of immune cells of the CSF can provide a non-invasive alternative to predict the response to ICI, as well as identify the T cell receptor clonotypes present in brain metastasis.