Loss-Function Learning for Digital Tissue Deconvolution

Adaptive digital tissue deconvolution

MOTIVATION

The 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.

RESULTS

We 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 IMPLEMENTATION

A python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281/zenodo.7548362).

Metabolic imbalance of T cells in COVID-19 is hallmarked by basigin and mitigated by dexamethasone

Metabolic pathways regulate immune responses and disrupted metabolism leads to immune dysfunction and disease. Coronavirus disease 2019 (COVID-19) is driven by imbalanced immune responses, yet the role of immunometabolism in COVID-19 pathogenesis remains unclear. By investigating 87 patients with confirmed SARS-CoV-2 infection, 6 critically ill non-COVID-19 patients, and 47 uninfected controls, we found an immunometabolic dysregulation in patients with progressed COVID-19. Specifically, T cells, monocytes, and granulocytes exhibited increased mitochondrial mass, yet only T cells accumulated intracellular reactive oxygen species (ROS), were metabolically quiescent, and showed a disrupted mitochondrial architecture. During recovery, T cell ROS decreased to match the uninfected controls. Transcriptionally, T cells from severe/critical COVID-19 patients showed an induction of ROS-responsive genes as well as genes related to mitochondrial function and the basigin network. Basigin (CD147) ligands cyclophilin A and the SARS-CoV-2 spike protein triggered ROS production in T cells in vitro. In line with this, only PCR-positive patients showed increased ROS levels. Dexamethasone treatment resulted in a downregulation of ROS in vitro and T cells from dexamethasone-treated patients exhibited low ROS and basigin levels. This was reflected by changes in the transcriptional landscape. Our findings provide evidence of an immunometabolic dysregulation in COVID-19 that can be mitigated by dexamethasone treatment.

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. Hence, several deconvolution methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles and closely correlated cell types highly depends on the set of genes undergoing deconvolution. In this work, we introduce AutoGeneS, a platform that automatically extracts discriminative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. AutoGeneS can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).

DTD: An R Package for Digital Tissue Deconvolution

Digital tissue deconvolution (DTD) estimates the cellular composition of a tissue from its bulk gene-expression profile. For this, DTD approximates the bulk as a mixture of cell-specific expression profiles. Different tissues have different cellular compositions, with cells in different activation states, and embedded in different environments. Consequently, DTD can profit from tailoring the deconvolution model to a specific tissue context. Loss-function learning adapts DTD to a specific tissue context, such as the deconvolution of blood, or a specific type of tumor tissue. We provide software for loss-function learning, for its validation and visualization, and for applying the DTD models to new data.