Biology subjects

Rubinstein, J. C.

Publications and source records attributed to Rubinstein, J. C..

4 recordsLinked to original sources

bioRxiv2026

Tumor cell-intrinsic mtDNA instability drives cGAS-dependent temporal remodeling of the melanoma immune microenvironment

Mitochondrial DNA (mtDNA) instability is an emerging feature of cancer, yet its biological effects on the tumor microenvironment (TME) and tumor progression remain incompletely defined. Using melanocyte-specific conditional silencing of mitochondrial transcription factor A (TFAM) in an autochthonous BrafV600E/Pten-/- melanoma model, we demonstrate that tumor cell-intrinsic mtDNA instability accelerates melanoma growth and metastatic dissemination in a cGAS-dependent manner. Transcriptomic profiling of early and late tumor stages revealed that mtDNA instability drives temporal remodeling of the tumor microenvironment (TME), characterized by an early type I interferon (IFN)-rich inflammatory state that licenses immunoregulatory programs as tumors progress. Tumor-associated macrophages emerged as the principal immune cell type reshaped by tumor cell-intrinsic mtDNA stress signaling, with early enrichment of inflammatory states and a shift toward efferocytosis-associated, tissue-adaptive states. The early mtDNA instability-associated macrophage signatures mapped to human melanoma states associated with favorable survival and therapy response, whereas the late signatures mapped to states associated with poor prognosis and therapy resistance across independent cohorts. Anti-PD-1 treatment delayed progression of mtDNA-instable tumors, consistent with a therapeutic sensitivity window associated with the early IFN-rich immune state. These findings establish tumor cell-intrinsic mtDNA instability as a driver of temporal immune-stromal remodeling that promotes melanoma progression and shapes therapeutic response.

immunology↗

bioRxiv2026

DIANNE: Segmentation-Free Localization of Histology Differential Attributes

Pathologist-guided distinctions within histology and spatial omic images provide insights into health and disease, with digital pathology leveraging artificial intelligence to automate such assessments. To train computational models, current digital pathology methods rely on upfront manual annotations, which are time-consuming to generate. Pre-annotation is poorly suited to investigating novel spatial behaviors--a major need driven by advances in spatial profiling--for which annotation criteria and data needs will be uncertain. To address these challenges, we present DIANNE, a digital pathology annotation support and discovery approach for rapid training and inference of spatial differential attributes, enabled by train-time Positive Class Mixup Augmentation. DIANNE can compute foundation model-derived segmentation-free localization of differential classifiers across whole slide H&E images within seconds on a workstation, enabling interactive investigation of spatial niches. Predictive models can be re-trained in real-time in response to patch or regional annotation changes, clarifying determinative biological attributes across slides from only a few dozen annotated patches. We demonstrate the effectiveness of DIANNE for tumor detection, artifact identification, and exploration of pancreatic, fetal membranes and kidney tissue structures. DIANNE also provides analogous capabilities for IHC, multiplex immunofluorescence, and registered spatial transcriptomic+H&E images. DIANNE is implemented in a Jupyter toolkit, enabling rapid development of high-resolution classifiers from weakly-supervised training. DIANNE provides a practical system to quantitatively understand known and novel spatial phenotypes.

pathology↗

bioRxiv2024

Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma

Resistance of BRAF-mutant melanomas to targeted therapy arises from the ability of cells to enter a persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. Using spatial transcriptomics in patient derived xenograft models, we capture clonal lineage evolution during treatment, finding the persister state to show increased oxidative phosphorylation, decreased proliferation, and increased invasive capacity, with central-to-peripheral gradients. Phylogenetic tracing identifies intrinsic- and acquired-resistance mechanisms (e.g. dual specific phosphatases, Reticulon-4, CDK2) and suggests specific temporal windows of potential therapeutic efficacy. Using deep learning to analyze histopathological slides, we find morphological features of specific cell states, demonstrating that juxtaposition of transcriptomics and histology data enables identification of phenotypically-distinct populations using imaging data alone. In summary, we define state change and lineage selection during melanoma treatment with spatiotemporal resolution, elucidating how choice and timing of therapeutic agents will impact the ability to eradicate resistant clones. Statement of SignificanceTumor evolution is accelerated by application of anti-cancer therapy, resulting in clonal expansions leading to dormancy and subsequently resistance, but the dynamics of this process are incompletely understood. Tracking clonal progression during treatment, we identify conserved, global transcriptional changes and local clone-clone and spatial patterns underlying the emergence of resistance.

cancer biology↗

bioRxiv2023

Nextflow Pipeline for Visium and H&E Data from Patient-Derived Xenograft Samples

HighlightsWe have developed an automated data processing pipeline to quantify mouse and human data from patient-derived xenograft samples assayed by Visium spatial transcriptomics with matched hematoxylin and eosin (H&E) stained image. We enable deconvolution of reads with Xenome, quantification of spatial gene expression from host and graft species with Space Ranger, extraction of B-allele frequencies, and splicing quantification with Velocyto. In the H&E image processing sub-workflow, we generate morphometric and deep learning-derived feature quantifications complementary to the Visium spots, enabling multi-modal H&E/expression comparisons. We have wrapped the pipeline into Nextflow DSL2 in a scalable, portable, and easy-to-use framework. SummaryWe designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole slide image (WSI), optimized for Patient-Derived Xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genomes input and stand-alone image analysis. We showed the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of imaging features of H&E data reveal similar patterns arising from the two data modalities.

bioinformatics↗