Biology subjects

Pages, A.

Publications and source records attributed to Pages, A..

2 recordsLinked to original sources

bioRxiv2024

CXCL8 secreted by immature granulocytes inhibits wildtype hematopoiesis in chronic myelomonocytic leukemia

Chronic myelomonocytic leukemia (CMML) is a severe myeloid malignancy with limited therapeutic options. Single-cell analysis of clonal architecture demonstrated early clonal dominance with few residual wildtype hematopoietic stem cells. Circulating myeloid cells of the leukemic clone and the cytokines they produce generate a deleterious inflammatory climate. Our hypothesis is that therapeutic control of the inflammatory component in CMML could contribute to stepping down disease progression. The present study explores the contribution of immature granulocytes (iGRANs) to CMML progression. iGRANs can be detected and quantified in the peripheral blood of patients by spectral and conventional flow cytometry. Their accumulation is a potent and independent poor prognostic factor. These cells belong to the leukemic clone and behave as myeloid-derived suppressor cells. Bulk and single cell RNA sequencing revealed a pro-inflammatory status of iGRAN that secrete multiple cytokines of which CXCL8 at the highest level. This cytokine inhibits the proliferation of wildtype but not CMML hematopoietic stem and progenitor cells (HSPCs) in which CXCL8 receptors are epigenetically downregulated. CXCL8 receptor inhibitors and CXCL8 blockade restore wildtype HSPC proliferation, suggesting that relieving CXCL8 selective pressure on wildtype HSPCs is a potential strategy to slow CMML progression and restore some healthy hematopoiesis.

cancer biology↗

bioRxiv2019

Simulated Plant Images Improve Maize Leaf Counting Accuracy

Automatically scoring plant traits using a combination of imaging and deep learning holds promise to accelerate data collection, scientific inquiry, and breeding progress. However, applications of this approach are currently held back by the availability of large and suitably annotated training datasets. Early training datasets targeted arabidopsis or tobacco. The morphology of these plants quite different from that of grass species like maize. Two sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait. Convolutional neural networks (CNNs) trained on entirely synthetic data provided predictive power for scoring leaf number in real-world images. This power was less than CNNs trained with equal numbers of real-world images, however, in some cases CNNs trained with larger numbers of synthetic images outperformed CNNs trained with smaller numbers of real-world images. When real-world training images were scarce, augmenting real-world training data with synthetic data provided improved prediction accuracy. Quantifying leaf number over time can provide insight into plant growth rates and stress responses, and can help to parameterize crop growth models. The approaches and annotated training data described here may help future efforts to develop accurate leaf counting algorithms for maize.

plant biology↗