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Biomedical subjects

Anna R Poetsch

Publications and source records attributed to Anna R Poetsch.

2 recordsLinked to original sources

A pancreatic cancer organoid-macrophage co-culture using starPEG-heparin hydrogel deciphers tumor-immune cell interactions.

Macrophages are among the most abundant immune cells in the pancreatic ductal adenocarcinoma (PDAC) tumor microenvironment (TME) and play a key role in regulating the immunosuppressive niche that facilitates tumor growth. Although recent three-dimensional (3D) culture systems using patient-derived materials have advanced our understanding of tumor biology, most models lack key cellular TME components and thus fail to capture tumor-immune cell interactions. To address this gap, we developed an in-vitro 3D co-culture model incorporating PDAC patient-derived organoids (PDOs) and macrophages within a synthetic hydrogel matrix. We optimized culture conditions by tuning medium and matrix conditions to support both cell lineages. Flow cytometry and transcriptomic analyses revealed that initially undifferentiated macrophages adopt an M2-like profile upon exposure to PDAC PDOs in starPEG-heparin hydrogels, mirroring the macrophage phenotypes observed by multiplex immunohistochemistry in the matched primary PDAC tissues. Cytokine secretome profiling revealed PDO-specific differences, indicating distinct underlying macrophage polarization subtypes. Collectively, our starPEG-heparin hydrogel-based 3D co-culture enables hypothesis-driven and physiologically relevant studies of tumor-macrophage interactions and may advance immune-modulatory treatment strategies in patients with PDAC.

Journal Article

Using the DNA language model, GROVER, to parse effects of sequence, chromatin and regulatory features on genome stability.

MOTIVATION: Genome stability is shaped by DNA sequence and chromatin context, but their relative contributions to double-strand break (DSB) sensitivity remain unclear. RESULTS: We show that the DNA language model, GROVER, can infer DSB location based on sequence. DSB hotspots tend to contain GC-rich sequences that belong to promoters, genes and short interspersed nuclear elements (SINEs). Additionally, we identified several specific short sequences (tokens) that are associated with modulating DSB sensitivity. Another model using chromatin and genome regulatory features outperforms the sequence-only model, highlighting complementary and cell-type specific information. Integrating sequence and genome biological features yields the best performance, demonstrating their synergy. Analyzing this model revealed that, dependent on the sample, genome stability information encoded in H3K36me3 and DNase-seq can be learned from the sequence, but not H3K27ac or H3K9me3. Embedding chromatin data directly into the GROVER architecture enabled cell-type specific modeling with performance matching the full chromatin feature model. Our results suggest that while chromatin and regulatory context provides important information, such as cell-type specificity, much of the information shaping DSB patterns is already encoded in the DNA sequence itself. Our integrative modeling approach not only reveals DSB patterns but also provides a generalizable strategy for tracing predictions in genomic data. AVAILABILITY: Data, models, and a tutorial are available on Zenodo.

Chromatin