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Saiselet, M.

Publications and source records attributed to Saiselet, M..

2 recordsLinked to original sources

Single nuclei and spatial transcriptomes suggest a stratification of papillary and anaplastic thyroid cancer cells

Sixty percent of papillary thyroid cancers (PTCs) are driven by BRAFV600E, a mutation associated with high inter- and intra-tumoral heterogeneity. PTCs may become highly aggressive anaplastic thyroid cancers (ATC). While single cell transcriptomics may resolve this heterogeneity, it is potentially confounded by technical effects whose correction may dampen inter-tumor variations. Here we profiled ATCs and BRAFV600E PTCs with single nuclei RNA-seq and spatial transcriptomics, and an experimental design disentangling biological and technical variations. It reveals that much transcriptional variation in cancer cells and several immune cell types is idiosyncratic, i.e. tumor-specific, a phenomenon obscured by batch integration in a number of single cell studies. Idiosyncrasies are associated in some cases with genomic aberrations and global tissue states like hypoxia. Beyond idiosyncrasies, differentiation markers SLC5A5 (NIS), TPO, TG and TSHR are lost in a sequence mirrored by their gain during human thyroid organoids maturation, suggesting a new classification of cancer cell states. PTC cells retain TSHR expression and show features of partial EMT with a massive expression of FN1, which promotes proliferation via an autocrine loop. In contrast, ATCs undergo full blown EMT, with expression of mesenchymal extracellular components and loss of TSHR. Finally, we show that the microenvironment of cancer cells is driven by inflammation. These findings may help future stratifications of BRAFV600E PTCs.

cancer biology↗

Transcriptional output, cell types densities and normalization in spatial transcriptomics

Spatial transcriptomics measures mRNA at hundreds of 100 micrometer-diameter spots evenly spread across 6.5x6.9 mm2 histological slices. Gene expression within each spot is commonly normalized by total read counts. However we show that the transcriptional output of individual spots reflects the number of cells they contain, hence total read counts per spot reflect relevant biology. Although per-spot read-count normalization reveals important enrichment trends, it may heavily distort cell-type-related absolute local expression and conceal important biological information.

genomics↗