Single Cell Sequencing

Autor: Prof. MUDr. Karel Smetana, DrSc.

Incidence of malignant tumours is increasing worldwide and necesitates novel therapeutical options and, in turn, detailed diagnosis. The diagnostic dataset is based on the combination of conventional  iochemistry, histology, immunohistochemistry and molecular features of tumours. Genetic profile of the patient can be used to personalize the care and employ targeted or biological therapy. This approach was revolutionized in recent years by introduction of the so called ‘-omics’ methods, which enabled to profile mutations and expression activity of the tumour mass at whole-genome scale.

It is accepted that tumours are heterogeneous. Beside the cancerous populations, they contain noncancerous cells such as immune cells and cancer-associated fibroblasts. These populations interact in many ways and create a malignant ecosystem. This ecosystem is an essential biological phenomenon and its understanding is crucial for succesfull therapy. However, the ‘-omics’ methods until recently analysed the tumour as a bulk tissue and  neglected its inner structure.

The introduction of single-cell sequencing can overcome the handicaps of the bulk tumour analyses. Every single cell in the tumour ecosystem can be characterised individually and transcription activity of its genes can be determined. Cell types and their subpopulations can be subsequently identified using robust bioinformatic analyses, which are made possible due to massive improvement in computational resources.

In this lecture, we present our understanding of the heterogeneity of normal dermal fibroblasts under the influence of melanoma cells in 3D in vitro model of heterogeneous spheres. Using single-cell sequencing, we categorized fibroblasts into three main groups based on the activity of (i) genes encoding extracellular matrix, (ii) ID genes and (iii) genes coding for proinflammatory factors and influencing TGF-β signalisation.

Prezentace ke stažení

• Prof. MUDr. Karel Smetana, DrSc. – Karel Smetana-Single Cell Sequencing