Biology subjects

Zimmerman, J. W.

Publications and source records attributed to Zimmerman, J. W..

4 recordsLinked to original sources

bioRxiv2024

Comparison of Different Approaches to Single Cell RNA Sequencing of Cancer Associated Fibroblasts

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with a poor prognosis. PDAC has a high propensity for metastasis, particularly to the lungs and liver. Cancer associated fibroblasts (CAFs) represent a major stromal component of PDAC with both tumor-promoting and restraining properties. Of note, CAFs play a significant role in the creation of an immunosuppressive tumor microenvironment (TME) and the metastasis of PDAC. Studies have demonstrated functional heterogeneity among different subpopulations of CAFs, highlighting the need to identify specific subpopulations when targeting CAFs. MethodsThe orthotopic model was used for both KPC-4545 and KPC-3403 cell lines, which were derived from the primary tumors of KPC mice with liver metastases and lung metastases only, respectively. In brief, 2x106 KPC cells were injected subcutaneously into the flanks of synergic female C57BI6 mice. Tumors were harvested and cut into 2-3 mm3 pieces before being implanted into the pancreas of new 6-8-week-old syngeneic female C57Bl/6 mice. Murine orthotopic tumors were dissected, mechanically and enzymatically processed with Miltenyi Tumor Dissociation Kit (Miltenyi Biotec) thirteen days after tumor implantation. Samples were filtered with a 100 {micro}m strainer, washed with T cell media, and centrifuged twice. Two different samples underwent single cell RNA-sequencing (scRNA-seq) for each cell line: an unenriched sample, which represents all cells following dissociation of the tumor, and a CAF-enriched sample. To further obtain the CAF-enriched sample, cells were then stained with CD45-AF657 (BioLegend clone 30-F11, 1:20), CD31-AF647 (BioLegend clone 390, 1:20), EPCAM-AF647 (BioLegend, clone G8.8, 1:20), and TER119-AF647 (BioLegend clone TER-119 1:20) for 30 minutes on ice. After two washes, CD45-, CD31-, EPCAM-, and TER119-negative cells, representing the CAF-enriched fraction, were obtained via cell sorting. scRNA-seq of both the unenriched and CAF-enriched fractions were performed using 10X Chromium microfluidic chips and data was analyzed using CellRanger v6.1.1, mm10 transcriptome reference, and 10X Loupe Browser. ResultsWe found that scRNA-seq of the unenriched whole tumor showed only one cluster of CAFs for both cells lines, making it difficult for studying CAF heterogeneity. Enriching for CAFs prior to scRNA-seq allowed for better capture of CAFs and provided more granularity on CAF heterogeneity for both KPC-4545 and KPC-3403. ConclusionsWhile enrichment provides more information on CAF heterogeneity, the process results in the loss of other cells within the TME. The need to capture CAF heterogeneity while studying cell-cell interaction between CAFs and other cells within the TME and identifying how distinct CAF populations respond differently to treatment warrants the use of other methods such as single-nuclear RNA-seq.

cancer biology↗

bioRxiv2023

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework--a cell behavior hypothesis grammar--that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

systems biology↗

bioRxiv2022

Spatial transcriptomics of FFPE pancreatic intraepithelial neoplasias reveals cellular and molecular alterations of progression to pancreatic ductal carcinoma

Spatial transcriptomics (ST) is a powerful new approach to characterize the cellular and molecular architecture of the tumor microenvironment. Previous single-cell RNA-sequencing (scRNA-seq) studies of pancreatic ductal adenocarcinoma (PDAC) have revealed a complex immunosuppressive environment characterized by numerous cancer associated fibroblasts (CAFs) subtypes that contributes to poor outcomes. Nonetheless, the evolutionary processes yielding that microenvironment remain unknown. Pancreatic intraepithelial neoplasia (PanIN) is a premalignant lesion with potential to develop into PDAC, but the formalin-fixed and paraffin-embedded (FFPE) specimens required for PanIN diagnosis preclude scRNA-seq profiling. We developed a new experimental pipeline for FFPE ST analysis of PanINs that preserves clinical specimens for diagnosis. We further developed novel multi-omics analysis methods for threefold integration of imaging, ST, and scRNA-seq data to analyze the premalignant microenvironment. The integration of ST and imaging enables automated cell type annotation of ST spots at a single-cell resolution, enabling spot selection and deconvolution for unique cellular components of the tumor microenvironment (TME). Overall, this approach demonstrates that PanINs are surrounded by the same subtypes of CAFs present in invasive PDACs, and that the PanIN lesions are predominantly of the classical PDAC subtype. Moreover, this new experimental and computational protocol for ST analysis suggests a biological model in which CAF-PanIN interactions promote inflammatory signaling in neoplastic cells which transitions to proliferative signaling as PanINs progress to PDAC. SummaryPancreatic intraepithelial neoplasia (PanINs) are pre-malignant lesions that progress into pancreatic ductal adenocarcinoma (PDAC). Recent advances in single-cell technologies have allowed for detailed insights into the molecular and cellular processes of PDAC. However, human PanINs are stored as formalin-fixed and paraffin-embedded (FFPE) specimens limiting similar profiling of human carcinogenesis. Here, we describe a new analysis protocol that enables spatial transcriptomics (ST) analysis of PanINs while preserving the FFPE blocks required for clinical assessment. The matched H&E imaging for the ST data enables novel machine learning approaches to automate cell type annotations at a single-cell resolution and isolate neoplastic regions on the tissue. Transcriptional profiles of these annotated cells enable further refinement of imaging-based cellular annotations, showing that PanINs are predominatly of the classical subtype and surrounded by PDAC cancer associated fibroblast (CAF) subtypes. Applying transfer learning to integrate ST PanIN data with PDAC scRNA-seq data enables the analysis of cellular and molecular progression from PanINs to PDAC. This analysis identified a transition between inflammatory signaling induced by CAFs and proliferative signaling in PanIN cells as they become invasive cancers. Altogether, this integration of imaging, ST, and scRNA-seq data provides an experimental and computational approach for the analysis of cancer development and progression.

cancer biology↗

bioRxiv2022

Inflammatory Signaling and Fibroblast-Cancer Cell Interactions Transfer from a Harmonized Human Single-cell RNA Sequencing Atlas of Pancreatic Ductal Adenocarcinoma to Organoid Co-Culture

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by a heterogeneous tumor microenvironment (TME) that is enriched with cancer associated fibroblasts (CAFs)1. Cell-cell interactions involving these CAFs promote an immunosuppressive phenotype with altered inflammatory gene expression. While single-cell transcriptomics provides a tool to dissect the complex intercellular pathways that regulate cancer-associated inflammation in human tumors, complementary experimental systems for mechanistic validation remain limited. This study integrated single-cell data from human tumors and novel organoid co-cultures to study the PDAC TME. We derived a comprehensive atlas of PDAC gene expression from six published human single-cell RNA sequencing (scRNA-seq) datasets2-7 to characterize intercellular signaling pathways between epithelial tumor cells and CAFs that regulate the inflammatory TME. Analysis of the epithelial cell compartment identified global gene expression pathways that modulate inflammatory signaling and are correlated with CAF composition. We then generated patient-derived organoid-CAF co-cultures to serve as a biological model of the cellular interactions learned from human tissue in the atlas. Transfer learning analysis to additional scRNA-seq data of this co-culture system and mechanistic experiments confirmed the epithelial response to fibroblast signaling. This bidirectional approach of complementary computational and in vitro applications provides a framework for future studies identifying important mechanisms of intercellular interactions in PDAC.

cancer biology↗