Biology subjects

Stephens, B. M.

Publications and source records attributed to Stephens, B. M..

5 recordsLinked to original sources

bioRxiv2023

Quantitative analysis of food web dynamics in a low export ecosystem

Food webs trace the flow of organic matter and energy among producers and consumers; for pelagic marine food webs, network complexity directly influences the amount and form of carbon exported to the deep ocean via the biological pump. Here we present a synoptic view of mixed layer food web dynamics observed during the late summer 2018 EXport Processes in the Ocean from Remote Sensing (EXPORTS) field campaign in the subarctic Northeast Pacific at the long-running time-series site, Ocean Station Papa. Carbon biomass reservoirs of phytoplankton, microzooplankton, and bacterioplankton, were approximately equal while mesozooplankton biomass was 70% lower. Live organisms composed [~]40% of the total particulate organic carbon within the mixed layer: the remainder was attributed to detritus. Rates of carbon transfer among reservoirs indicated production and assimilation rates were well balanced by losses, leaving little organic carbon available for export. The slight positive net community production rate generated organic carbon that was exported from the system in the form of food web byproducts, such as large fecal pellets generated by mesozooplankton. This characteristically regenerative food web had relatively slow turnover times with small-magnitude transfers of carbon relative to standing stocks that occurred amidst a high background concentration of detrital particles and dissolved organic matter. The concurrent estimation of food web components and rates revealed that separated processes dominated the transfer of carbon within the food web compared to those that contributed to export. Plain Language SummaryThe biological carbon pump drives a downward flux of organic matter from the sunlit surface ocean to the vast ocean interior. Ecological interactions in the surface ocean directly affect the amount and type of carbon that is exported to the deep ocean. In this study, we present a synthesis of the late summer mixed layer food web in the Northeast Pacific that was extensively characterized during the 2018 EXport Processes in the Ocean from Remote Sensing (EXPORTS) field campaign. We found the majority of carbon was recycled within the mixed layer by microbes through multiple transfers between producers and consumers. Larger organisms, mesozooplankton and salps, only consumed a small amount of carbon but through the formation of sinking fecal pellets were the main mechanism of transporting carbon out of the system. The study highlights the need to concurrently study microbial and large organism dynamics to develop a predictive understanding of the fate of organic carbon in the oceans. Key PointsO_LIThe microbial loop dominated carbon flow in the late summer mixed layer food web of the North Pacific, most net production was respired leaving little carbon available for export. C_LIO_LIActive production and consumption of organic carbon occurred amid a high background of detrital particulate organic carbon (58% of total) with slow turnover time, 66 d. C_LIO_LIMesozooplankton which had relatively minor carbon consumption rates created the majority of export production due to efficient repackaging of consumed material. C_LI

ecology↗

bioRxiv2022

Transcriptomic Profiling of Plasma Extracellular Vesicles Enables Reliable Annotation of the Cancer-specific Transcriptome and Molecular Subtype

Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predict consensus molecular subtypes in metastatic colorectal cancer patients. We further demonstrate the ability to monitor changes in transcriptomic subtype under treatment selection pressure and identify molecular pathways in evRNA associated with recurrence. Our approach also identified expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling. Statement of significanceWe have developed an approach to interrogate changes in cancer molecular subtypes and differentially expressed genes, through the analysis and deconvolution of RNA sequencing of plasma EVs. Serial analyses of tumor-encoded transcriptomes in liquid biopsies can enable facile cancer detection and monitor for recurrences and therapy-induced tumor evolution.

cancer biology↗

bioRxiv2021

Occult polyclonality of preclinical pancreatic cancer models drives in vitro evolution.

Intratumoral heterogeneity (ITH) is a hallmark of cancer. The advent of single-cell technologies has helped uncover ITH in a high-throughput manner in different cancers across varied contexts. Here we apply single-cell sequencing technologies to reveal striking ITH in assumptively oligoclonal pancreatic ductal adenocarcinoma (PDAC) cell lines. Our findings reveal a high degree of both genomic and transcriptomic heterogeneity in established and globally utilized PDAC cell lines, custodial variation induced by growing apparently identical PDAC cell lines in different laboratories, and profound transcriptomic shifts in transitioning from 2D to 3D spheroid growth models. Our findings also call into question the validity of widely available immortalized, non-transformed pancreatic lines as contemporaneous "control" lines in experiments. Further, while patient-derived organoid (PDOs) are known to reflect the cognate in vivo biology of the parental tumor, we identify transcriptomic shifts during ex vivo passage that might hamper their predictive abilities over time. The impact of these findings on rigor and reproducibility of experimental data generated using established preclinical PDAC models between and across laboratories is uncertain, but a matter of concern.

cancer biology↗

bioRxiv2020

Elucidation of tumor-stromal heterogeneity and the ligand-receptor interactome by single cell transcriptomics in real-world pancreatic cancer biopsies

Precision medicine approaches in pancreatic ductal adenocarcinoma (PDAC) are imperative for improving disease outcomes. However, the long-term fidelity of recently deployed ex vivo preclinical platforms, such as patient-derived organoids (PDOs) remains unknown. Through single-cell RNA sequencing (scRNA-seq), we identify substantial transcriptomic evolution of PDOs propagated from the parental tumor, which may alter predicted drug sensitivity. In contrast, scRNA-seq is readily applicable to limited biopsies from human primary and metastatic PDAC and identifies most cancers as being an admixture of previously described epithelial transcriptomic subtypes. Integrative analyses of our data provide an in-depth characterization of the heterogeneity within the tumor microenvironment, including cancer-associated fibroblast (CAF) subclasses, and predicts for a multitude of ligand-receptor interactions, revealing potential targets for immunotherapy approaches. While PDOs continue to enable prospective therapeutic prediction, our analysis also demonstrates the complementarity of using orthogonal de novo biopsies from PDAC patients paired with scRNA-seq to inform clinical decision-making. Statement of SignificanceThe application of single-cell RNA sequencing to diagnostic pancreatic cancer biopsies provides in-depth transcriptomic characterization of the tumor epithelium and microenvironment, while minimizing potential artifacts introduced by an intervening ex vivo passaging step. Thus, this approach can complement the use of patient-derived organoids in implementing precision oncology.

cancer biology↗

bioRxiv2018

Single Cell Transcriptomics of Pancreatic Cancer Precursors Demonstrates Epithelial and Microenvironmental Heterogeneity as an Early Event in Neoplastic Progression

BackgroundEarly detection of pancreatic ductal adenocarcinoma (PDAC) remains elusive. Precursor lesions of PDAC, specifically, intraductal papillary mucinous neoplasms (IPMNs) represent a bona fide pathway to invasive neoplasia, although the molecular correlates of progression remain to be fully elucidated. Single cell transcriptomics provides a unique avenue for dissecting both the epithelial and microenvironmental heterogeneity that accompany multistep progression from non-invasive IPMNs to PDAC.\n\nMethodsSingle cell RNA-sequencing was performed through droplet-based sequencing on 5,403 cells from two low-grade IPMNs (LGD-IPMN), two high-grade IPMNs (HGD-IPMN), and two PDACs (all surgically resected).\n\nResultsAnalysis of single cell transcriptomes revealed heterogeneous alterations within the epithelium and the tumor microenvironment during the progression of non-invasive dysplasia to invasive cancer. While HGD-IPMNs expressed many core-signaling pathways described in PDAC, LGD-IPMNs harbored subsets of single cells with a transcriptomic profile that overlapped with invasive cancer. Notably, a pro-inflammatory immune component was readily seen in low-grade IPMNs, comprised of cytotoxic T-cells, activated T-helper cells, and dendritic cells, which was progressively depleted during neoplastic progression, accompanied by infiltration of myeloid-derived suppressor cells. Finally, stromal myofibroblast populations were heterogeneous, and acquired a previously described tumor-promoting and immune-evading phenotype during invasive carcinogenesis.\n\nConclusionsThis study demonstrates the ability to perform high resolution profiling of the transcriptomic changes that occur during multistep progression of cystic PDAC precursors to cancer. Notably, single cell analysis provides an unparalleled insight into both the epithelial and microenvironmental heterogeneity that accompany early cancer pathogenesis, and might be a useful substrate to identify targets for cancer interception.

pathology↗