Biology subjects

Adams, J. P.

Publications and source records attributed to Adams, J. P..

3 recordsLinked to original sources

bioRxiv2026

A single-cell and multi-platform spatial atlas of the human pancreas resolves a transformation-associated epithelial axis and a recurrent boundary-organized tumour microenvironment

Most pancreatic ductal adenocarcinoma (PDAC) single-cell and spatial studies analyze one cohort or platform, obscuring recurrent biology. We assembled a human pancreas single-cell and single-nucleus reference of 1,186,130 cells from 19 studies and interpreted 176 Visium sections comprising 458,877 spots across non-diseased pancreas, chronic pancreatitis, PanIN, IPMN, primary PDAC and metastasis. Marker-supported labels were used after two RNA-based copy- number callers failed known-diploid controls. BANKSY domains, two reference-mapping methods and sample-level analyses resolved a cross-sectional epithelial axis extending from acinar-rich to malignant tissue. Five trajectory algorithms recovered similar ordering on a shared embedding; their consensus was interpreted as transformation-associated, not temporal or clonal. Malignant regions were globally segregated from fibroblast and myeloid compartments. Signed- distance analysis refined this pattern into a malignant core, a CAF/myeloid surround beginning at the tumour boundary and a more distal lymphoid compartment. Candidate extracellular-matrix communication, led by COLLAGEN, LAMININ and FN1, concentrated at the interface. Changes were reproduced in six patient-matched Normal-tumour pairs using exact patient-level tests. Visium HD resolved the same organization at single-cell resolution and showed that 8-um bins distorted immune-adjacency estimates. Xenium also revealed recurrent neighbourhoods but sample-specific stromal boundaries. We provide a confound-aware framework for identifying recurrent epithelial and microenvironmental organization in PDAC.

cancer biology↗

bioRxiv2023

Single-cell transcriptomics reveals that tumor-infiltrating natural killer cells are activated by localized ablative immunotherapy and share anti-tumor signatures induced by immune checkpoint inhibitors

RationaleNatural killer (NK) cells provide protective anti-cancer immunity. However, the cancer therapy induced activation gene signatures and pathways in NK cells remain unclear. MethodsWe applied a novel localized ablative immunotherapy (LAIT) by synergizing photothermal therapy (PTT) with intra-tumor delivering of the immunostimulant N-dihydrogalactochitosan (GC), to treat breast cancer using a mammary tumor virus-polyoma middle tumor-antigen (MMTV-PyMT) mouse model. We performed single-cell RNA sequencing (scRNAseq) analysis to unveil the cellular heterogeneity and compare the transcriptional alterations induced by PTT, GC, and LAIT in NK cells within the tumor microenvironment (TME). ResultsScRNAseq showed that NK subtypes, including cycling, activated, interferon-stimulated, and cytotoxic NK cells. Trajectory analysis revealed a route toward activation and cytotoxicity following pseudotime progression. Both GC and LAIT elevated gene expression associated with NK cell activation, cytolytic effectors, activating receptors, IFN pathway components, and cytokines/chemokines in NK subtypes. Single-cell transcriptomics analysis using immune checkpoint inhibitor (ICI)-treated animal and human samples revealed that ICI-induced NK activation and cytotoxicity across several cancer types. Furthermore, ICI-induced NK gene signatures were also induced by LAIT treatment. We also discovered that several types of cancer patients had significantly longer overall survival when they had higher expression of genes in NK cells that were also specifically upregulated by LAIT. ConclusionOur findings show for the first time that LAIT activates cytotoxicity in NK cells and the upregulated genes positively correlate with beneficial clinical outcomes for cancer patients. More importantly, our results further establish the correlation between the effects of LAIT and ICI on NK cells, hence expanding our understanding of mechanism of LAIT in remodeling TME and shedding light on the potentials of NK cell activation and anti-tumor cytotoxic functions in clinical applications.

immunology↗

bioRxiv2019

Engineering a seven enzyme biotransformation using mathematical modelling and characterized enzyme parts

Multi-step enzyme reactions offer considerable cost and productivity benefits. Process models offer a route to understanding the complexity of these reactions, and allow for their optimization. Despite the increasing prevalence of multi-step biotransformations, there are few examples of process models for enzyme reactions. From a toolbox of characterized enzyme parts, we demonstrate the construction of a process model for a seven enzyme, three step biotransformation using isolated enzymes. Enzymes for cofactor regeneration were employed to make this in vitro reaction economical. Good modelling practice was critical in evaluating the impact of approximations and experimental error. We show that the use and validation of process models was instrumental in realizing and removing process bottlenecks, identifying divergent behavior, and for the optimization of the entire reaction using a genetic algorithm. We validated the optimized reaction to demonstrate that complex multi-step reactions with cofactor recycling involving at least seven enzymes can be reliably modelled and optimized.\n\nSignificance statementThis study examines the challenge of modeling and optimizing multi-enzyme cascades. We detail the development, testing and optimization of a deterministic model of a three enzyme cascade with four cofactor regeneration enzymes. Significantly, the model could be easily used to predict the optimal concentrations of each enzyme in order to get maximum flux through the cascade. This prediction was strongly validated experimentally. The success of our model demonstrates that robust models of systems of at least seven enzymes are readily achievable. We highlight the importance of following good modeling practice to evaluate model quality and limitations. Examining deviations from expected behavior provided additional insight into the model and enzymes. This work provides a template for developing larger deterministic models of enzyme cascades.

biochemistry↗