Computational dissection of cellular distributions and cell-type specific regulation patterns from spatial transcriptomics data

Kreftvev inneholder ikke bare kreftceller, men også en rekke forskjellige immunceller. Noen av dem er nyttige, andre bytter side og hjelper nå svulsten. Kunnskapen om fordelingen av immuncelletyper til en pasient er svært viktig. Denne informasjonen er med på å definere den beste personlige behandlingen, gi en klarere overlevelsesprognose eller hjelper å definere nye mulige behandlingsalternativer. I tillegg gjennomgår immunceller i tumormikromiljøet flere endringer, noen er nyttige i kampen mot kreft, andre hjelper svulsten til å vokse eller spre seg. Kunnskapen om disse endringene i de ulike immuncelletypene hjelper til med å forklare prosessene i svulsten eller til å oppdage mulige nye biomarkører for behandlingsalternativer. I dette prosjektet ønsker vi å ta hensyn til både den romlige fordelingen og endringene av de forskjellige immuncelletypene avhengig av deres lokalisering i forhold til kreftvev. Hvilke endringer kan vi finne? Hvordan påvirker de hverandre og hvordan påvirkes de av svulsten? Vi ønsker å bruke denne kunnskapen til å oppdage nye mulige biomarkører for behandlingsalternativer.

Spatial transcriptomics provides unprecedented details about the molecular mechanisms that take place within and between cells. ST assesses the molecular content of well-defined regions in a tissue which potentially cover contributions from multiple cells. In order to draw correct biological conclusions from ST analysis, spatial distributions of individual cell types are a prerequisite. This generates an urgent need for computational methods addressing this question. However, computational estimates of cellular compositions from ST are complicated by several factors. First, the methods require well curated data which is usually not available; every specimen has its own individual characteristics and so, every tissue type or even every patient, might require a own, comprehensively verified model. Second, data is noisy, making estimates prone to errors. Moreover, even if a computational model is designed for a specific tissue or even patient, it might be miss-specified, leading to additional systematic errors. In this project, we will develop a toolbox called Computational Dissection of Cellular Distributions (CDCD), which directly accounts for these issues. CDCD will advance the state of the art by (1) taking into account a priori unknown, hidden cellular background contributions, (2) taking into account environmental factors (i.e., tissue and patient dependence), and by (3) comprehensive errors estimation. Intriguingly, in contrast to available techniques, CDCD will facilitate estimates of cellular regulation in space! Such gene-regulation gradients will allow us to address questions of how immune cells adapt their molecular phenotype with respect to their environment. Additionally we will verify our algorithms on a conjoint norwegian phase II melanoma case study. CDCD will be made freely available via easy-to-use webpage and software solution, enabling both expert bioinformaticians and wet-lab biologists an efficient use of the to-be-developed methodology.