Biology subjects

Ackerman, H. D.

Publications and source records attributed to Ackerman, H. D..

4 recordsLinked to original sources

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

bioRxiv2026

Ex Vivo Culture of Patient-Derived Primary, Metastatic, and Post-Mortem Lung Cancer Reveals Targetable States of EMT and Metabolic Plasticity

Lung cancer is a highly heterogeneous disease and remains the leading cause of cancer-related mortality worldwide. While mouse models and patient-derived organoids have advanced our understanding of lung cancer, key interactions within the tumor microenvironment (TME) remain poorly characterized. We developed microtumor models from lung adenocarcinoma (LUAD) and small cell lung cancer (SCLC) using mouse and patient samples, including surgical resections and rapid autopsy specimens. Microtumors preserve structural, cellular, and molecular features of the native TME, enabling mechanistic studies of tumor progression ex vivo. Multi-omics analyses of LUAD microtumors revealed progression-associated changes, including increased epithelial-to-mesenchymal transition (EMT) and metabolic reprogramming toward fatty acid synthesis. Pharmacologic inhibition of fatty acid synthesis through ACC1/2 reduced proliferation in patient-derived microtumors, identifying a targetable vulnerability. This platform provides a robust system for studying tumor progression, therapeutic response, and resistance mechanisms in lung cancer, including culturing postmortem specimens that are not accessible in current models.

cancer biology↗

bioRxiv2025

TAp73 mediates anti-tumor immunity through regulation of lipid metabolism in the lung tumor microenvironment

While immunotherapy has become the standard of care for lung adenocarcinoma (LUAD) patients without actionable genomic alterations, only a subset of patients benefits from a long-lasting response to immunotherapy. Activation of p53-related signals has emerged as a potential mediator of the lung tumor microenvironment (TME). Given that mutant-p53 interacts with p73 extensively and TAp73-deficient mice develop LUAD, we engineered a mouse model with conditional deletion of TAp73 to understand the interactions of the p53 family in the TME and in metabolic pathways that impact anti-tumor immunity. We demonstrated that TAp73 exerts a tumor-suppressive role in KrasG12D-driven LUAD by regulating lipid metabolism in the TME. We identified a TAp73-driven transcriptional signature involving genes in the arachidonic acid metabolism pathway operational in tumor-associated macrophages that favors T-cell activation and thus anti-tumor immunity. Similar transcriptional changes are seen in macrophages from LUAD patients with p53 mutations and in association with response to immunotherapy. SIGNIFICANCEThere is a need to understand how the LUAD TME impacts patient response to immunotherapy. We identified a transcriptional program enacted by TAp73 in tumor alveolar macrophages that supports T-cell activation. Transcriptional and metabolomic data from LUAD patients supports the relevance of this program in response to immune checkpoint inhibition.

cancer biology↗

bioRxiv2022

The Origin Of Cysteine and its Catabolism in Mammalian Tissues and Tumors

Cysteine plays critical roles in cellular biosynthesis, enzyme catalysis, and is an essential contributor to redox metabolism. While cultured cells are highly dependent on exogenous cystine for proliferation and survival, how diverse tissues obtain and use cysteine in vivo has not been characterized. We comprehensively interrogated cysteine metabolism in normal murine tissues and the cancer that arise from them using stable isotope 13C-serine and 13C-cystine tracing. We found that de novo cysteine synthesis was highest in normal liver and pancreas and absent in lung tissue. In tumors, cysteine synthesis was either inactive or downregulated during tumorigenesis. By contrast, cystine uptake and metabolism to downstream metabolites was a universal feature of normal tissues and tumors. Differences in cysteine catabolism were evident across tumor types, including glutathione synthesis. Thus, cystine is a major contributor to the cysteine pool in tumors and cysteine catabolic pathways are differentially active across tumor types.

cancer biology↗