P2X receptors are ATP-gated cation channels composed of one or more of seven different subunits. P2X receptors participate in intestinal neurotransmission but the subunit composition of enteric P2X receptors is unknown. In this study, we used tissues from P2X3 wild-type (P2X3+/+) mice and mice in which the P2X3 subunit gene had been deleted (P2X3-/-) to investigate the role of this subunit in neurotransmission in the intestine. RT-PCR analysis of mRNA from intestinal tissues verified P2X3 gene deletion. Intracellular electrophysiological methods were used to record synaptic and drug-induced responses from myenteric neurons in vitro. Drug-induced longitudinal muscle contractions were studied in vitro. Intraluminal pressure-induced reflex contractions (peristalsis) of ileal segments were studied in vitro using a modified Trendelenburg preparation. Gastrointestinal transit was measured as the progression in 30 min of a liquid radioactive marker administered by gavage to fasted mice. Fast excitatory postsynaptic potentials recorded from S neurons (motoneurons and interneurons) were similar in tissues from P2X3+/+ and P2X3-/- mice. S neurons from P2X3+/+ and P2X3-/- mice were depolarized by application of ATP but not alpha,beta-methylene ATP, an agonist of P2X3 subunit-containing receptors. ATP and alpha,beta-methylene ATP induced depolarization of AH (sensory) neurons from P2X3+/+ mice. ATP, but not alpha,beta-methylene ATP, caused depolarization of AH neurons from P2X3-/- mice. Peristalsis was inhibited in ileal segments from P2X3-/- mice but longitudinal muscle contractions caused by nicotine and bethanechol were similar in segments from P2X3+/+ and P2X3-/- mice. Gastrointestinal transit was similar in P2X3+/+ and P2X3-/- mice. It is concluded that P2X3 subunit-containing receptors participate in neural pathways underlying peristalsis in the mouse intestine in vitro. P2X3 subunits are localized to AH (sensory) but not S neurons. P2X3 receptors may contribute to detection of distention or intraluminal pressure increases and initiation of reflex contractions.

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With our ability to take and quantify numerous complex images of cells and cell populations, the ability to paint an accurate picture of the underlying data has never been more valuable. Deferring from the contemporary classics in data visualization to methods that exploit advances in artificial intelligence is an essential step in understanding high-throughput, three-dimensional microscopy data. This feature article discusses how generating or simulating representative cells that may not exist in the data set, yet summarize the underlying distribution, allows researchers to effectively and efficiently analyse cellular morpho-dynamics. Furthermore, learning from these artificial intelligence-based techniques allows us to ‘see what the machine is seeing’ in a step towards unpacking the chaos of cell biology to understand the very fundamentals of living organisms.

Introduction Locally advanced oesophageal adenocarcinoma is typically treated with neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) followed by surgery. Significant benefit to neoadjuvant treatment however is confined to a minority of patients (<25%) and there are no reliable means of establishing prior to treatment in whom this benefit will occur. In this study, we assessed the utility of features extracted from high-resolution digital microscopy of pre-treatment biopsies in predicting response to neoadjuvant therapy in a machine-learning based modelling framework. Method A total of 102 cases were included in the study. Pre-treatment clinical information, including TNM staging, was obtained, along with diagnostic biopsies. Diagnostic biopsies were converted into high-resolution whole slide-images and features extracted using a pre-trained convolutional neural network (Xception). Elastic net regression models were then trained and validated with bootstrapping with 1000 resampled datasets. The response was considered according to Mandard tumour regression grade (TRG). Result There were 45 (44.1%) responders (TRG1-2) and 57 (57%) non-responders (TRG3-5) in the dataset. 34 patients (33.3%) received NACT and 68 (66.7%) received NACRT. A model trained with RNA-seq data achieved fair performance only in predicting response (AUC 0.598 95% CI 0.593–0.603), which was far exceeded by use of segmented diagnostic biopsy images (AUC 0.872 95% CI 0.869–0.875), which also produced well calibrated predictions of risk. Conclusion Despite using a small dataset, impressive performance in classifying response to neoadjuvant treatment can be achieved, particularly using automated image classification. Further study to refine the methodology is required before expansion to clinical settings. Take-home Message Response to neoadjuvant treatment for oesophageal cancer can be predicted from diagnostic biopsies