Görtler, F. | Search bioRxiv

bioRxiv2023

An integrated RNA-proteomic landscape of drug induced senescence in a cancer cell line

Senescent cells are characterized by an arrest in proliferation. In addition to replicative senescence resulting from telomere exhaustion, sub-lethal genotoxic stress resulting from DNA damage, oncogene activation, mitochondrial dysfunction or reactive metabolites also elicits a senescence phenotype. Senescence is a controlled programme affecting a wide variety of biological processes with some core hallmarks of senescence as well as tissue specific changes. This study presents an integrative multi-omic analysis of proteomic and RNA-seq from proliferating and senescent osteosarcoma cells. This study demonstrates senescence induction in a widely used cell line which can be used as a model system for characterising cancer cell responses to sub-lethal doses of chemotherapeutic agents, and makes available both RNA-seq and proteomic data from proliferating and senescent cells in open access repositories to aid reuse by the community.

cell biology↗

bioRxiv2023

Adaptive Digital Tissue Deconvolution

MotivationThe inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g., a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions. ResultsWe propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes. Availability and implementationA python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281/zenodo.7548362). Contactmichael.altenbuchinger@bioinf.med.uni-goettingen.de and sushma.grellscheid@uib.no Supplementary informationSupplementary material is available at Bioinformatics online.

bioinformatics↗