Biology subjects

Raue, A.

Publications and source records attributed to Raue, A..

5 recordsLinked to original sources

bioRxiv2026

bulk2scDiff: A Pseudobulk-Conditioned Diffusion Model for Bulk-to-Single-Cell RNASeq Generation

Bulk RNA sequencing remains the predominant profiling strategy for large clinical cohorts, but it aggregates transcriptional signals across cell populations, thereby masking the underlying cellular heterogeneity. Inferring this heterogeneity from existing bulk transcriptomic data could extend large cohort-based studies that have already been profiled, but constitutes an underdetermined inverse problem, as one bulk profile can be compatible with multiple underlying cellular populations. Existing computational deconvolution methods address this problem primarily by estimating cell-type proportions or cell-type-averaged expression profiles rather than resolving expression at the level of individual cells. Here, we present bulk2scDiff, a proof-of-concept conditional diffusion framework that reformulates bulk-to-single-cell inference as conditional generation of single-cell expression profiles from pseudobulk transcriptomic input. We evaluated bulk2scDiff on two cancer single-cell RNA sequencing datasets, breast cancer and acute myeloid leukemia, where pseudobulk profiles were derived from the single-cell data and used as conditioning inputs, with the matched single-cell populations providing ground truth for controlled evaluation. Across both cases, bulk2scDiff closely reconstructed populations from training samples and generated biologically coherent single-cell populations for held-out samples, generalizing most consistently to recurrent immune features. A pseudobulk-swap control further confirmed sample-specific conditioning, with each sample corresponding pseudobulk yielding the closest agreement with its observed population in nearly all cases. Overall, our work establishes the feasibility of conditional diffusion for generating single-cell populations from pseudobulk transcriptomic profiles, providing a foundation for future evaluation with clinical bulk RNA sequencing data.

bioinformatics↗

bioRxiv2026

Integration of proteomic data from cell lines and tumors

Cancer cell lines are widely used in preclinical research, yet the clinical translation of findings from cell lines remains limited. Identifying cell lines that best resemble patient tumors requires integration of molecular profiles across biologically distinct sample types. Advances in transcriptomic integration have demonstrated the potential of deep learning for aligning data across different sample types. However, comparable approaches for proteomic data integration remain lacking, potentially because of the prevalence of missing values in proteomic datasets. Here, we introduce ProtInt, a deep learning-based framework that integrates proteomic data by combining principles from proteomic imputation and transcriptomic integration methods. We applied ProtInt to integrate label-free proteomic profiles from 771 cancer cell lines and 550 treatment-naive tumors, and showed that ProtInt outperformed batch correction and transcriptomic integration methods. Comparison of the cell line proteomes before and after integration revealed recurrent increase of proteins associated with immune reaction and reduction of proteins involved in mitochondrial gene expression as proteomes of cell lines were adapted to resemble tumors. These results establish ProtInt as a framework for joint analysis of proteomic datasets across distinct sample types and may facilitate the identification of cell lines best suited for clinically relevant studies.

bioinformatics↗

bioRxiv2021

Single-cell immune profiling with TCR clonotype barcoding identifies biomarker signatures that predict response to immune checkpoint blockade

The identification of predictive biomarkers for patient treatment response is urgently needed to increase the probability of success of existing and novel experimental therapies. Single-cell profiling has provided novel biological insights into drug responses in the tumor microenvironment, but its potential for biomarker discovery has not been fully explored for therapeutic purposes. We describe a novel approach to discover predictive response biomarkers from single-cell data from a small patient cohort using the T cell receptor sequence intrinsic to each T cell to match clonotypes between pre- and post-treatment tumor samples. As a result, we have identified a predictive gene expression signature for immune checkpoint blockade and validated its predictive performance using data from three larger clinical studies. Our results demonstrated that applying clonotyping with single-cell genomic profiling is a promising novel approach for biomarker identification that does not require data collected from large patient cohorts. This could increase success rates, reduce clinical trial size, and significantly impact future clinical developments of immunomodulatory therapeutics.

immunology↗

bioRxiv2017

Predicting Ligand-Dependent Tumors From Multi-Dimensional Signaling Features

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using a novel approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo.\n\nSummaryPrediction of ligand-induced growth of cancer cell lines, which correlates with ligand-blocking antibody efficacy, could be significantly improved by learning from features of a mechanistic signaling model, and was applied to reveal a correlation between growth factor expression and predicted response in patient samples.

systems biology↗

bioRxiv2017

Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

As interactions between the immune system and tumour cells are governed by a complex network of cell-cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patients response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication- and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types as well as three T cell subtypes. As the ratios of CD4+, CD8+ and regulatory T cells have been shown to predict overall survival, we extended our analysis to include the estimation of prognostic ratios that may enable the application in a clinical setting. Using the tumour derived RGEPs, we can estimate, for the first time, the content of cancer associated fibroblasts, endothelial cells and the malignant cells in a patient sample by a deconvolution approach. In addition, improved tumour cell gene expression profiles can be obtained by this method by computationally removing contamination from non-malignant cells. Given the difficulty around sample preparation and storage to obtain high quality single-cell RNA-seq data in the clinical context, the presented method represents a computational solution to derive the cellular composition of a tissue sample.

bioinformatics↗