Biology subjects

Geuenich, M. J.

Publications and source records attributed to Geuenich, M. J..

6 recordsLinked to original sources

bioRxiv2025

Integrated spatial proteomics of human PDAC uncovers an expanded tumour-immune-stroma spectrum with genomic associations

Distinctively, pancreatic ductal adenocarcinoma (PDAC) consists of sparse tumour lesions intertwined with extensive desmoplastic stroma. The complexity of tumour-microenvironment interactions within this desmoplasia poses a challenge for accurate tumour profiling and patient stratification, and characterizes a profoundly chemoresistant tumour. Here we mapped the spatial relationships between tumour, stroma, and immune cell compartments delineating tumour and microenvironment types that expand the classical to basal spectrum of human PDAC. We used imaging mass cytometry to profile the in situ multi-cellular organization of 81 cell types in resected cases with paired whole genome sequencing. Cell types, functions, and pathway activation were distributed as highly reproducible environments in discrete locations throughout these tumours, which we deep-profiled using laser-capture mass spectrometry. We show that the connections between tumour phenotypes, vascularization, immune response, and stromal biophysical state are reinforced by genomic aberrations, altered by treatment, and associated with patient outcome. Predictive machine-learning models showed that spatial single cell data outperformed genomic or clinical features but integrated multi-omics models provide the best prediction of patient survival with compressed models requiring only 10 non-redundant robust molecular measures associated with the phenotypic spectrum of PDAC. Together, these findings define a phenotypic and molecular framework of PDAC that captures tumour-microenvironment co-dependencies and offers a refined basis for patient stratification and therapeutic targeting.

cancer biology↗

bioRxiv2024

Interactive design and validation of antibody panels using single-cell RNA-seq atlases

Single-cell RNA-sequencing holds promise for identifying novel markers of cellular variation for antibody-based technologies. However, antibody panel design is often difficult due to multiple experimental and biological constraints. We introduce Cytomarker, an interactive platform enabling human-in-the-loop design of antibody panels from single-cell transcriptomic data. We use Cytomarker to spatially profile human mammary tissue subpopulations and develop a novel antibody screening approach to validate granular subpopulation predictions across >3.5M cells.

bioinformatics↗

bioRxiv2024

Decoding multicellular niche formation in the tumour microenvironment from nonspatial single-cell expression data

Accurate inference of granular cell states that co-occur within the tumour microenvironment (TME) is central to defining pro- and anti-tumour environments. Here, we describe how ecosystems of cell populations can be robustly inferred from nonspatial single-cell RNA-seq (scRNA-seq) atlases. Leveraging a unique discovery-validation setup across eight scRNA-seq datasets profiling pancreatic ductal adenocarcinoma (PDAC), we show highly consistent co-occurrence of fine-grained cell states across patients and characterize the positive predictive value of such analyses. Building on this, we develop a novel probabilistic model to quantify multi-cellular ecosystems directly from such atlas-scale scRNA-seq datasets. By mapping these ecosystems to spatial transcriptomics data, we demonstrate that such ecosystems represent bona fide spatial variation within individual tumours despite being learned from nonspatial data. Importantly, through mapping these ecosystems across two large clinical cohorts, we show they are more predictive of therapy response than any individual cell state and are associated with specific tumour somatic mutations. Together, this work lays the foundation for inferring reproducible multicellular ecosystems directly from large nonspatial scRNA-seq atlases.

bioinformatics↗

bioRxiv2023

The impacts of active and self-supervised learning on efficient annotation of single-cell expression data

A crucial step in the analysis of single-cell data is annotating cells to cell types and states. While a myriad of approaches have been proposed, manual labeling of cells to create training datasets remains tedious and time-consuming. In the field of machine learning, active and self-supervised learning methods have been proposed to improve the performance of a classifier while reducing both annotation time and label budget. However, the benefits of such strategies for single-cell annotation have yet to be evaluated in realistic settings. Here, we perform a comprehensive benchmarking of active and self-supervised labeling strategies across a range of single-cell technologies and cell type annotation algorithms. We quantify the benefits of active learning and self-supervised strategies in the presence of cell type imbalance and variable similarity. We introduce several heuristic procedures tailored to single-cell data -- including a marker-aware adaptive reweighting cell selection procedure -- that shows competitive performance with existing approaches. In addition, we demonstrate that having prior knowledge of cell type markers improves annotation accuracy. Finally, we summarize our findings into a set of recommendations for those implementing cell type annotation procedures or platforms. An R package implementing the heuristic approaches introduced in this work may be found at https://github.com/camlab-bioml/leader.

bioinformatics↗

bioRxiv2021

Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data

The creation of scalable single-cell and highly-multiplexed imaging technologies that profile the protein expression and phosphorylation status of heterogeneous cellular populations has led to multiple insights into disease processes including cancer initiation and progression. A major analytical challenge in interpreting the resulting data is the assignment of cells to a priori known cell types in a robust and interpretable manner. Existing approaches typically solve this by clustering cells followed by manual annotation of individual clusters or by strategies that gate protein expression at predefined thresholds. However, these often require several subjective analysis choices such as selecting the number of clusters and do not automatically assign cell types in line with prior biological knowledge. They further lack the ability to explicitly assign cells to an unknown or uncharacterized type, which exist in most highly multiplexed imaging experiments due to the limited number of markers quantified. To address these issues we present Astir, a probabilistic model to assign cells to cell types by integrating prior knowledge of marker proteins. Astir uses deep recognition neural networks for fast Bayesian inference, allowing for cell type annotations at the million-cell scale and in the absence of previously annotated reference data across multiple experimental modalities and antibody panels. We demonstrate that Astir outperforms existing approaches in terms of accuracy and robustness by applying it to over 2.1 million single cells from several suspension and imaging mass cytometry and microscopy datasets in multiple tissue contexts. We further showcase that Astir can be used for the fast analysis of the spatial architecture of the tumour microenvironment, automatically quantifying the immune influx and spatial heterogeneity of patient samples. Astir is freely available as an open source Python package at https://www.github.com/camlab-bioml/astir.

bioinformatics↗

bioRxiv2020

Loss of MGA mediated Polycomb repression promotes tumor progression and invasiveness

MGA, a transcription factor and member of the MYC network, is mutated or deleted in a broad spectrum of malignancies. As a critical test of a tumor suppressive role, we inactivated Mga in two mouse models of non-small cell lung cancer using a CRISPR based approach. MGA loss significantly accelerated tumor growth in both models and led to de-repression of atypical Polycomb PRC1.6, E2F and MYC-MAX targets. Similarly, MGA depletion in human lung adenocarcinoma lines augmented invasive capabilities. We further show that MGA, E2F6 and L3MBTL2 co-occupy thousands of promoters and that MGA stabilizes PRC1.6 subunits. Lastly, we report that MGA loss has also a pro-growth effect in human colon organoids. Our studies establish MGA as a bona fide tumor suppressor in vivo and suggest a tumor suppressive mechanism in adenocarcinomas resulting from widespread transcriptional attenuation of MYC and E2F targets mediated by an atypical Polycomb complex containing MGA-MAX dimers.

cancer biology↗