1. Chemical reprogramming of human somatic cells to pluripotent stem cells | Nature
chemical stimulation by exposure to small molecules offers an alternative approach that can manipulate cell fate in a simple and highly controllable manner8,9,10. However, human somatic cells are refractory to chemical stimulation owing to their stable epigenome2,11,12 and reduced plasticity13,14;
Here we demonstrate, by creating an intermediate plastic state, the chemical reprogramming of human somatic cells to human chemically induced pluripotent stem cells that exhibit key features of embryonic stem cells.
The whole chemical reprogramming trajectory analysis delineated the induction of the intermediate plastic state at the early stage, during which chemical-induced dedifferentiation occurred, and this process was similar to the dedifferentiation process that occurs in axolotl limb regeneration.
Moreover, we identified the JNK pathway as a major barrier to chemical reprogramming, the inhibition of which was indispensable for inducing cell plasticity and a regeneration-like program by suppressing pro-inflammatory pathways.
The c-Jun N-terminal kinase (JNK) pathway is one of the major signaling cassettes of the mitogen-activated protein kinase (MAPK) signaling pathway. It functions in the control of a number of cellular processes, including proliferation, embryonic development and apoptosis. (JNK Signaling Pathway - Creative Diagnostics (creative-diagnostics.com))
2. CAR T cell killing requires the IFNγR pathway in solid but not liquid tumours | Nature
Chimeric antigen receptor (CAR) therapy has had a transformative effect on the treatment of haematologic malignancies1,2,3,4,5,6, but it has shown limited efficacy against solid tumours.
Solid tumours may have cell-intrinsic resistance mechanisms to CAR T cell cytotoxicity. Here, to systematically identify potential resistance pathways in an unbiased manner, we conducted a genome-wide CRISPR knockout screen in glioblastoma, a disease in which CAR T cells have had limited efficacy7,8.
We found that the loss of genes in the interferon-γ receptor (IFNγR) signalling pathway (IFNGR1, JAK1 or JAK2) rendered glioblastoma and other solid tumours more resistant to killing by CAR T cells both in vitro and in vivo. However, loss of this pathway did not render leukaemia or lymphoma cell lines insensitive to CAR T cells. Using transcriptional profiling, we determined that glioblastoma cells lacking IFNγR1 had lower upregulation of cell-adhesion pathways after exposure to CAR T cells.
We found that loss of IFNγR1 in glioblastoma cells reduced overall CAR T cell binding duration and avidity. The critical role of IFNγR signalling in susceptibility of solid tumours to CAR T cells is surprising, given that CAR T cells do not require traditional antigen-presentation pathways.
Instead, in glioblastoma tumours, IFNγR signalling was required for sufficient adhesion of CAR T cells to mediate productive cytotoxicity. Our work demonstrates that liquid and solid tumours differ in their interactions with CAR T cells and suggests that enhancing binding interactions between T cells and tumour cells may yield improved responses in solid tumours.
3. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning | Nature Biotechnology
A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of identifying the precise boundary of every cell in an image.
To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets.
We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm.
...
Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.
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