Bernd Bodenmiller

Serval: A modular framework for decoding imaging based spatial transcriptomics data

Serval is a modular framework for decoding spatial transcriptomics data that introduces Cosine, a method improving transcript recovery and correlation with expression references.

Modelling interpretable patient-level representationsfrom structured and simple multimodal data

The paper introduces FACTMx, a variational framework that jointly models structured and simple patient-level data to learn interpretable representations, showing improved performance in simulations and applications to cancer and cardiovascular cohorts.

Multimodal cell communication networks nominate immunotherapies for RCC subgroups with discrete T cell recruitment or expansion

The paper integrates multi-modal data to identify immune subgroups in RCC and suggests immunotherapy strategies based on distinct T cell communication networks.

Humanized patient-derived xenografts preserve tumour-specific immune microenvironments

The paper shows humanized PDX models preserve tumour-specific immune microenvironments, supporting their use for studying immune evasion mechanisms.

SpaCEy: Discovery of Functional Spatial Tissue Patterns by Association with Clinical Features Using Explainable Graph Neural Networks

SpaCEy identifies spatial tissue patterns linked to clinical outcomes using explainable graph neural networks in proteomic cancer data.

Exploiting pair correlation function to describe biological tissue structure

This paper introduces PCF-SiM, a framework for quantifying spatial organization in multiplexed imaging data, demonstrating its application in detecting tissue remodeling and spatial interactions in disease models.

Compressed sensing expands the multiplexity of imaging mass cytometry

The paper extends compressed sensing to imaging mass cytometry, enabling higher multiplexity by recovering spatial protein expression from fewer measured channels.

Cellohood: multi-granular discovery of cellular neighborhoods with a permutation-invariant set transformer auto-encoder

Cellohood identifies cellular neighborhoods in spatial transcriptomics data, revealing clinically relevant patterns across multiple granularity levels.

In vitro and in vivo derived macrophages occupy distinct phenotypic states

The paper compares in vitro and in vivo macrophage phenotypes using mass cytometry, finding distinct surface marker expression patterns between macrophages derived from culture and those from tumor-associated environments.

Multi-modal image analysis for large scale cancer tissue studies within IMMUcan

The paper presents two workflows for analyzing multiplexed imaging data from cancer tissues, applied to over 10,000 samples from 2500 patients, enabling large-scale tumor microenvironment studies.

Spatial proteomics with 100+ markers with Highly Multiplexed MALDI-IHC

This paper introduces a method for high-plex protein imaging using MALDI-IHC with PCMT-labeled antibodies and presents an open-source analysis pipeline called gutenTAG.

Imaging mass cytometry reveals early β-cell dysfunction and changes in immune signatures during type 1 diabetes progression in human pancreata

The paper uses imaging mass cytometry to analyze human pancreatic samples and identifies early β-cell dysfunction and immune changes in type 1 diabetes progression.

An end-to-end workflow for multiplexed image processing and analysis

The paper introduces a workflow for processing and analyzing multiplexed imaging data, integrating image processing with single-cell analysis tools.

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data

cytoviewer provides an R/Bioconductor package for interactive visualization of multiplexed imaging data, enabling quality control and hypothesis testing through user-friendly tools.

Statistical modeling and analysis of multiplexed imaging data

The paper develops statistical models for multiplexed imaging data to improve differential abundance testing and proposes a sampling strategy to enhance statistical power.

Optimizing multiplexed imaging experimental design through tissue spatial segregation estimation

The paper develops a statistical framework to estimate tissue spatial segregation, enabling optimal design of multiplexed imaging experiments by determining the necessary fields of view to capture all cell types.

scROSHI - robust supervised hierarchical identification of single cells

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction

The paper uses single-cell proteomics and perturbation experiments to improve drug sensitivity predictions in breast cancer and identify genomic features associated with treatment response.

Expansion Sequencing: Spatially Precise In Situ Transcriptomics in Intact Biological Systems

ExSeq enables multiplexed RNA mapping at nanoscale resolution by expanding biological specimens for in situ sequencing.

cytomapper: an R/Bioconductor package for visualisation of highly multiplexed imaging data

cytomapper visualises multiplexed imaging data at pixel and cell levels, enabling analysis of spatial expression and cell phenotypes in diabetes patients.

A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids

The paper quantifies how environment, neighborhood, and cell state influence phenotypic variability in 3D spheroids using mass cytometric imaging and a linear model.

Multidimensional single-cell modeling of cellular signaling

This paper introduces DISCO, a method to infer single-cell signaling dynamics from snapshot data, revealing cell-to-cell variation in MAPK/ERK signaling and its dependence on initial states.

SCIM: Universal Single-Cell Matching with Unpaired Feature Sets

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.

Embedding single-cell experimental conditions to reveal manifold structure of cancer drug perturbation effects

This paper introduces PhEMD, a method to embed drug perturbation effects in single-cell experiments, revealing low-dimensional manifold structures and enabling efficient drug effect inference.

Modelling cell-cell interactions from spatial molecular data with spatial variance component analysis

This paper introduces SVCA, a method to analyze spatial molecular data and quantify the impact of cell-cell interactions on gene expression in breast cancer tissue.

Analysis of the human kinome and phosphatome reveals diseased signaling networks induced by overexpression

The paper analyzes the human kinome and phosphatome to identify signaling networks altered by protein overexpression, revealing novel relationships and mechanisms of cancer progression.

Channel crosstalk correction in suspension and imaging mass cytometry

The paper introduces a bead-based compensation workflow for mass cytometry to correct channel crosstalk, improving data quality and panel development.

Systematic analysis of cell phenotypes and cellular social networks in tissues using the histology topography cytometry analysis toolbox (histoCAT)

The paper introduces histoCAT, a tool for analyzing single-cell spatial omics data to explore cell phenotypes and interactions in tissues, demonstrating its application on human breast cancer mass cytometry images.

Learning Edge Rewiring in EMT from Single Cell Data

The paper develops methods to infer dynamic gene regulatory network rewiring during EMT from single-cell data and validates their predictive power for drug effects.