Biology subjects

Hartmann, F. J.

Publications and source records attributed to Hartmann, F. J..

7 recordsLinked to original sources

bioRxiv2026

UELer: a Jupyter-based framework for interactive exploration of multiplexed imaging datasets

Summary Multiplexed imaging and spatial proteomics generate complex datasets that require both computational analysis and visual inspection. However, these tasks mostly occur in separate environments because interactive viewers generally require a local display or an additional data server beyond the remote Jupyter sessions itself where large datasets are computationally analyzed. We here present UELer, an interactive viewer that links multi-channel image views with quantitative analysis results directly within Jupyter notebooks, requiring no dedicated infrastructure beyond the notebook session. Cells selected through computational analysis and summary plots can be inspected directly in their tissue context, and selections made in the image can be made available to any downstream analysis. Together, these capabilities support interactive data exploration, iterative cell annotation, and reproducible retrieval of selected regions. Availability and Implementation UELer is a Python package built on ipywidgets and runs in Jupyter environments supporting ipywidgets 8.1 or later, tested in JupyterLab and Visual Studio Code on Linux, macOS, and Windows. It is freely available under GPL-3.0 license and can be installed via pip. Source code and documentation are available at https://github.com/HartmannLab/UELer and https://hartmannlab.github.io/UELer/. An online, no-install version runs remotely via BinderHub (https://mybinder.org/v2/gh/HartmannLab/UELer/main), accessible through the script/run_ueler_binder.ipynb notebook.

bioinformatics↗

bioRxiv2026

Loss of the Y chromosome drives epigenetic and transcriptomic plasticity in lung adenocarcinoma

Loss of the Y chromosome (LOY) is associated with poor survival across multiple solid tumors, yet the underlying molecular mechanisms remain poorly understood. Here, we identify LOY as a central driver of lineage plasticity and epigenetic heterogeneity in lung adenocarcinoma. Integrating multi-omic profiling of primary samples with isogenic cellular models, we show that LOY triggers epithelial-to-mesenchymal transition (EMT). Mechanistically, LOY causes haploinsufficiency of dosage-sensitive regulators, leading to widespread DNA hypomethylation at EMT gene promoters, including THY1 and LOX. Single-cell multi-omic analyses demonstrate that LOY induces epigenetic heterogeneity, destabilizes the chromatin landscape, and increases lineage plasticity, enabling rapid cellular adaptation to metabolic and genotoxic stress. Moreover, LOY-induced plasticity facilitates tumor engraftment and metastatic dissemination in vivo. These findings establish Y-linked gene dosage as a critical guardian of epigenetic stability, providing a mechanistic rationale for how its loss amplifies phenotypic diversity and lineage plasticity, ultimately driving adverse clinical outcomes in LOY patients.

cancer biology↗

bioRxiv2026

Cell-of-Origin, not Oncogenic Effect, Determines esmoplastic Immune Exclusion in KRAS-Driven Liver Cancer

Intrahepatic cholangiocarcinoma (iCCA) and hepatocellular carcinoma (HCC) are the two most common primary liver cancers and share common risk factors. Yet they exhibit distinct oncogenic driver landscapes and fundamentally different tumor microenvironments (TME), with iCCA characterised by dense desmoplastic stroma that limits therapeutic efficacy. Whether these differences reflect oncogenic context or the developmental lineage of the cancer cell has remained unresolved. Here, using syngeneic orthotopic murine models derived from CRISPR-engineered cholangiocyte and hepatocyte organoids each carrying Trp53 deletion and KrasG12D mutation, we show that cell-of-origin, not oncogenic pathway activation, is the dominant determinant of TME architecture. Spatial proteomics of [~]390,000 cells reveals that cholangiocyte-derived tumors develop a stromal barrier of peripherally enriched SMA+ cancer-associated fibroblasts (CAFs) that physically excludes immune cells and elevates PD-1/PD-L1 engagement, whereas hepatocyte derived tumors permit broader immune infiltration. Transcriptional variance partitioning confirms lineage as the primary source of gene expression divergence. Integrating murine and human transcriptomic and secretomic datasets, we identify LAMC2 and uPA as cholangiocyte lineage-specific secreted factors that trigger CAF activation. Genetic deletion of either factor markedly impairs iCCA formation in vivo. These findings establish that lineage-encoded secretory programmes create a desmoplastic and immune-excluded stroma and identify LAMC2 and uPA as functionally relevant modulators of TME in KRAS-driven iCCA.

cancer biology↗

bioRxiv2022

Supervised dimensionality reduction for exploration of single-cell data by Hybrid Subset Selection - Linear Discriminant Analysis

Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction enables visualization of data by representing cells in two-dimensional plots that capture the structure and heterogeneity of the original dataset. Visualizations contribute to human understanding of data and are useful for guiding both quantitative and qualitative analysis of cellular relationships. Existing algorithms are typically unsupervised, utilizing only measured features to generate manifolds, disregarding known biological labels such as cell type or experimental timepoint. Here, we repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling users to tailor visualizations to separate specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this flexible, computationally-efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes, such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality reduction algorithms and illustrate its utility and versatility for exploration of single-cell mass cytometry, transcriptomics and chromatin accessibility data.

bioinformatics↗

bioRxiv2020

Multiplexed Single-cell Metabolic Profiles Organize the Spectrum of Human Cytotoxic T Cells

Cellular metabolism regulates immune cell activation, differentiation and effector functions to the extent that its perturbation can augment immune responses. However, the analytical technologies available to study cellular metabolism lack single-cell resolution, obscuring metabolic heterogeneity and its connection to immune phenotype and function. To that end, we utilized high-dimensional, antibody-based technologies to simultaneously quantify the single-cell metabolic regulome in combination with phenotypic identity. Mass cytometry (CyTOF)-based application of this approach to early human T cell activation enabled the comprehensive reconstruction of the coordinated metabolic remodeling of naive CD8+ T cells and aligned with conventional bulk assays for glycolysis and oxidative phosphorylation. Extending this analysis to a variety of tissue-resident immune cells revealed tissue-restricted metabolic states of human cytotoxic T cells, including metabolically repressed subsets that expressed CD39 and PD1 and that were enriched in colorectal carcinoma versus healthy adjacent tissue. Finally, combining this approach with multiplexed ion beam imaging by time-of-flight (MIBI-TOF) demonstrated the existence of spatially enriched metabolic neighborhoods, independent of cell identity and additionally revealed exclusion of metabolically repressed cytotoxic T cell states from the tumor-immune boundary in human colorectal carcinoma. Overall, we provide an approach that permits the robust approximation of metabolic states in individual cells along with multimodal analysis of cell identity and functional characteristics that can be applied to human clinical samples to study cellular metabolism how it may be perturbed to affect immunological outcomes.

immunology↗

bioRxiv2019

An integrated multi-omic single cell atlas to redefine human B cell memory

To evaluate the impact of heterogeneous B cells in health and disease, comprehensive profiling is needed at a single cell resolution. We developed a highly-multiplexed screen to quantify the co-expression of 351 surface molecules on low numbers of primary cells. We identified dozens of differentially expressed molecules and aligned their variance with B cell isotype usage, metabolism, biosynthesis activity, and signaling response. Here, we propose a new classification scheme to segregate peripheral blood B cells into ten unique subsets, including a CD45RB+ CD27- early memory population and a CD19hi CD11c+ memory population that is a potent responder to immune activation. Furthermore, we quantify the contributions of antibody isotype and cell surface phenotype to various cell processes and find that phenotype largely drives B cell function. Taken together, these findings provide an extensive profile of human B cell diversity that can serve as a resource for further immunological investigations.

immunology↗

bioRxiv2018

Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy

The success of immunotherapy has led to a myriad of new clinical trials. Connected to these trials are efforts to discover biomarkers providing mechanistic insight and predictive signatures for personalization. Still, the plethora of immune monitoring technologies can face investigator bias, missing unanticipated cellular responses in limited clinical material. We here present a mass cytometry workflow for standardized, systems-level biomarker discovery in immunotherapy trials. To broadly enumerate human immune cell identity and activity, we established and extensively assessed a reference panel of 33 antibodies to cover major cell subsets, simultaneously quantifying activation and immune checkpoint molecules in a single assay. The resulting assay enumerated [≥] 98% of peripheral immune cells with [≥] 4 positively identifying antigens. Robustness and reproducibility were demonstrated on multiple samples types, across research centers and by orthogonal measurements. Using automated analysis, we monitored complex immune dynamics, identifying signatures in bone-marrow transplantation associated graft-versus-host disease. This validated and available workflow ensures comprehensive immunophenotypic analysis, data comparability and will accelerate biomarker discovery in immunomodulatory therapeutics.

immunology↗