GIGA platforms go single cell !

GIGA platforms go single cell !

Following a training at the Karolinska Institute in Stockholm, a team of scientists at ULg’s GIGA, combining expertise in histology, NGS, imaging, and bioinformatics, is implementing Spatial Transcriptomics as a new service of GIGA-Technology platforms.

The advent of Next Generation Sequencing (NGS) technologies has boosted genomic sciences to unprecedented perspectives with the characterization of an impressive number of genomes for various organisms and individuals, healthy or affected. NGS technologies have also been extensively used to determine transcriptomic signatures for specific tissues or in particular conditions. So far, these strategies have mostly been applied to tissues or organs, which usually correspond to complex mixtures of very different types of cells. Although very informative already, such “bulk” sequencing however prevents from apprehending the cellular complexity and from identifying subtle modifications happening in specific cell types, conditions or locations in a tissue. Over the past couple of years have emerged technologies enabling to overcome these limitations: single cell RNA sequencing (scRNA-seq) and Spatial Transcriptomics.

In scRNA-seq, cells from a complex heterogeneous mixture are first isolated by capture in micro-wells or through encapsulation in micro-droplets using microfluidic devices (Gawad C. et al., Nature reviews: genetics, 17, 175-188. (2016)). Thousands of cells can then be sequenced in parallel thanks to the incorporation during cDNA synthesis of cell-specific DNA barcodes as well as unique molecular identifiers. Once traced back to their originating cells, the sequencing reads can be used to identify the different cell types present in the initial mixture irrespective of any preconceived composition. Given the increasing popularity of scRNA-seq, several alternatives have been developed and commercialized. The GIGA-Genomics platform has acquired a 10X Chromium System enabling to generate transcriptomic profiles for thousands of single cells in a single run.

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