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.
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.
Background Standard of care for locally advanced oesophageal adenocarcinoma is neoadjuvant chemotherapy or chemoradiotherapy followed by surgery. Only a minority of patients (<25%) derive significant survival benefit from neoadjuvant treatment and there are no reliable means of establishing prior to treatment in whom this benefit will occur. Moreover, accurate prediction of survival prior to treatment is also not possible. The availability of machine learning techniques provides the potential to use complex data sources to answer these problems. In this study, we assessed the utility of high-resolution digital microscopy of pre-treatment biopsies in predicting both response to neoadjuvant therapy and overall survival. Methods A total of 157 cases were included in the study. Pre-treatment clinical information, including neoadjuvant treatment, was obtained, along with diagnostic biopsies. Diagnostic biopsies were converted into high-resolution whole slide-images and features extracted using the pre-trained convolutional neural network Xception. Single representative images were converted into patches from which predictive models were trained. Elastic net regression classifiers were derived and validated with bootstrapping and 1000 resampled datasets. The response to treatment was considered according to Mandard tumour regression grade (TRG). Model performance was quantified using the C-index (for TRG) and time-dependent AUC (tAUC, fo Overall survival) along with calibration plots. Results Median survival was 78.9months (95%CI 35.9 months – not reached). Survival at 5-years was 52.1%. Neoadjuvant treatment was received by 123 patients (78.3%), with a significant response seen in 45 cases (36.6%). A response was more likely in those patients who received chemoradiotherapy than chemotherapy (53.3% vs 23.1% p < 0.001) and in older patients (median age 69.4 vs 66.0 years, p = 0.038), with other characteristics similar. A predictive model for response to neoadjuvant treatment derived from image features and clinical data achieved good discrimination (C-index 0.767, 95%CI 0.701-0.833) and calibration. Accuracy of prediction of overall survival was more modest (tAUC 0.640, 95%CI 0.518-0.762). Conclusions Using a small dataset, utility of a feature extraction pipeline in prediction of patient level outcomes has been demonstrated. This was more marked in prediction of response to neoadjuvant treatment than overall survival, which may reflect the importance of pre-treatment clinical data in determining the former outcome. Further study to refine the methodology and confirmation in larger datasets are required before expansion to clinical settings.
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
Aberrations in cell geometry are linked to cell signalling and disease. For example, metastatic melanoma cells alter their shape to invade tissues and drive disease. Despite this, there is a paucity of methods to quantify cell shape in 3D and little understanding of the shape-space cells explore. Currently, most descriptions of cell shape rely on predefined measurements of cell regions or points along a perimeter. The adoption of 3D tissue culture and imaging systems in medical research has recently created a growing need for comprehensive 3D shape descriptions of cells. We have addressed this need using unsupervised geometric deep learning to learn shape representations of cells from 3D microscopy images of metastatic melanoma cells embedded in collagen tissue-like matrices. We used a dynamic graph convolutional foldingnet autoencoder with improved deep embedded clustering to simultaneously learn lower-dimensional representations and classes of 3D cell shapes from a dataset of more than 70,000 drug-treated melanoma cells imaged by high throughput light-sheet microscopy. We propose describing cell shape using 3D quantitative morphological signatures, which represent a cell's similarity to shape modes in the dataset, and are a direct output from our model. We used the extracted features to reveal the extent of the cell shape landscape and found that the shapes learned could predict drug treatment (up to 86% accuracy) and cell microenvironment, and are explainable. In particular, we found strikingly similar deep learning shape signatures between cells treated with microtubule polymerisation inhibitors and branched actin inhibitors. Finally, we implemented our methods as a Python package for ease of use by the medical research community.
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