The Impact of Freezing Temperature and Pretreatment Protocol on Solid Phase HR-MAS-MRS Metabolomes of Colorectal Cancer Tissue

Background: To explore the best pretreatment method of colorectal cancer tissue samples for metabolomics research based on solid-phase nuclear magnetic resonance. Method: Taking mucosal tissues of colorectal cancer and divide it into 5 groups of 0.2cm*0.2cm*0.2cm. Pretreatment was performed as follows: I. Liquid nitrogen storage; II. Transfer to the -80℃ refrigerator after storing in liquid nitrogen for 10 minutes; III. Transfer to the -80℃ refrigerator after storing in liquid nitrogen for 20 minutes; IV. Transfer to the -80℃ refrigerator after storing in liquid nitrogen for 30 minutes; V. -80℃ refrigerator storage. The interval between tumor sample separation to pretreatment is less than 30 minutes. The tissue sample testing process is carried out on Bruker AVII-600 Spectrometer equipped with a high-resolution probe having a 1H/13C magical angle rotation. The tissue samples were put into the NMR which run at a speed of 5000Hz for 10 minutes. NMR signals were collected and analyzed by Fourier transform, partial least squares discrimination analysis (PLS-DA). Corresponding metabolites and metabolic pathways were found in Human Metabolome Database (HMDB) according to the ppms with variable importance of projection (VIP) ＞1. Results: The content of pelargonic acid, stearic acid, D-Ribose, heptadecanoic acid, pyruvic acid, succinate, sarcosine, glycine, creatine, and L-lactate in liquid nitrogen storage group were significantly lower than the other groups (P<0.05), the content of glycerophosphocholine in liquid nitrogen storage group was lower than the other groups (P=0.055). Pyruvic, succinate and L-lactate are participating in glucose metabolism. Glycerophosphocholine, sarcosine, glycine and creatine are participating in choline phospholipid metabolism. This indicated that the glucose and choline phospholipid metabolism levels of the liquid nitrogen group were significantly lower than those of the other 4 groups. Conclusion: Liquid nitrogen storage can slow down the glucose and choline phospholipid metabolism process of colorectal cancer tissue samples in vitro; liquid nitrogen can preserve tissue sample’s metabolic state in the body. It is therefore the better way to store tissue sample than the other methods. clinical trial registry website: http://www.chictr.org.cn/index.aspx. Trial number: ChiCTR1900024640

Tackling Tumour Cell Heterogeneity at the Super-Resolution Level in Human Colorectal Cancer Tissue

Tumour cell heterogeneity, and its early individual diagnosis, is one of the most fundamental problems in cancer diagnosis and therapy. Single molecule localisation microscopy (SMLM) resolves subcellular features but has been limited to cultured cell lines only. Since nuclear chromatin architecture and microRNAs are critical in metastasis, we introduce a first-in-field approach for quantitative SMLM-analysis of chromatin nanostructure in individual cells in resected, routine-pathology colorectal carcinoma (CRC) patient tissue sections. Chromatin density profiles proved to differ for cells in normal and carcinoma colorectal tissues. In tumour sections, nuclear size and chromatin compaction percentages were significantly different in carcinoma versus normal epithelial and other cells of colorectal tissue. SMLM analysis in nuclei from normal colorectal tissue revealed abrupt changes in chromatin density profiles at the nanoscale, features not detected by conventional widefield microscopy. SMLM for microRNAs relevant for metastasis was achieved in colorectal cancer tissue at the nuclear level. Super-resolution microscopy with quantitative image evaluation algorithms provide powerful tools to analyse chromatin nanostructure and microRNAs of individual cells from normal and tumour tissue at the nanoscale. Our new perspectives improve the differential diagnosis of normal and (metastatically relevant) tumour cells at the single-cell level within the heterogeneity of primary tumours of patients.

Deep Learning Techniques for the Classification of Colorectal Cancer Tissue

It is very important to make an objective evaluation of colorectal cancer histological images. Current approaches are generally based on the use of different combinations of textual features and classifiers to assess the classification performance, or transfer learning to classify different organizational types. However, since histological images contain multiple tissue types and characteristics, classification is still challenging. In this study, we proposed the best classification methodology based on the selected optimizer and modified the parameters of CNN methods. Then, we used deep learning technology to distinguish between healthy and diseased large intestine tissues. Firstly, we trained a neural network and compared the network architecture optimizers. Secondly, we modified the parameters of the network layer to optimize the superior architecture. Finally, we compared our well-trained deep learning methods on two different histological image open datasets, which comprised 5000 H&E images of colorectal cancer. The other dataset was composed of nine organizational categories of 100,000 images with an external validation of 7180 images. The results showed that the accuracy of the recognition of histopathological images was significantly better than that of existing methods. Therefore, this method is expected to have great potential to assist physicians to make clinical diagnoses and reduce the number of disparate assessments based on the use of artificial intelligence to classify colorectal cancer tissue.