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Frisen, J.

Publications and source records attributed to Frisen, J..

21 records · Page 2Linked to original sources

High-density spatial transcriptomics arrays for in situ tissue profiling

Tissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays either lack global transcriptome information or are not at the single-cell level. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are then randomly deposited in individual wells on a slide. The barcoded beads are decoded and coupled to a specific spatial address. We then capture and spatially in situ label RNA from the same histological tissue sections placed on the bead array slide. HDST recovers hundreds of thousands of transcript-coupled barcodes per experiment at 2 m resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to 2D spatial analysis of tissues at high resolution.

genomics↗

Multidimensional transcriptomics provides detailed information about immune cell distribution and identity in HER2+ breast tumors

The comprehensive analysis of tumor tissue heterogeneity is crucial for determining specific disease states and establishing suitable treatment regimes. Here, we analyze tumor tissue sections from ten patients diagnosed with HER2+ breast cancer. We obtain and analyze multidimensional, genome-wide transcriptomics data to resolve spatial immune cell distribution and identity within the tissue sections. Furthermore, we determine the extent of immune cell infiltration in different regions of the tumor tissue, including invasive cancer regions. We combine cross-sectioning and computational alignment to build three-dimensional images of the transcriptional landscape of the tumor and its microenvironment. The three-dimensional data clearly demonstrates the heterogeneous nature of tumor-immune interactions and reveal interpatient differences in immune cell infiltration patterns. Our study shows the potential for an improved stratification and description of the tumor-immune interplay, which is likely to be essential in treatment decisions.

cancer biology↗

Conbase: a software for discovery of clonal somatic mutations in single cells through read phasing

Here we report the development of Conbase, a software application for the identification of somatic mutations in single cell DNA sequencing data with high rates of allelic dropout and at low read depth. Conbase leverages data from multiple samples in a dataset and utilizes read phasing to call somatic single nucleotide variants and to accurately predict genotypes in whole genome amplified single cells in somatic variant loci. We demonstrate the accuracy of Conbase on simulated datasets, in vitro expanded fibroblasts and clonally in vivo expanded lymphocyte populations isolated directly from a healthy human donor.

bioinformatics↗