Clinical challenges of tissue preparation for spatial transcriptome.
Xiaoxia LiuYujia JiangDongli SongLinlin ZhangGuang XuRui HouYong ZhangJian ChenYunfeng ChengLongqi LiuXun XuGang ChenDuojiao WuTianxiang ChenAo ChenXiangdong WangPublished in: Clinical and translational medicine (2022)
Spatial transcriptomics is considered as an important part of spatiotemporal molecular images to bridge molecular information with clinical images. Of those potentials and opportunities, the excellent quality of human sample preparation and handling will ensure the precise and reliable information generated from clinical spatial transcriptome. The present study aims at defining potential factors that might influence the quality of spatial transcriptomics in lung cancer, para-cancer, or normal tissues, pathological images of sections and the RNA integrity before spatial transcriptome sequencing. We categorised potential influencing factors from clinical aspects, including patient selection, pathological definition, surgical types, sample harvest, temporary preservation conditions and solutions, frozen approaches, transport and storage conditions and duration. We emphasis on the relationship between the combination of histological scores with RNA integrity number (RIN) and the unique molecular identifier (UMI), which is determines the quality of of spatial transcriptomics; however, we did not find significantly relevance between them. Our results showed that isolated times and dry conditions of sample are critical for the UMI and the quality of spatial transcriptomic samples. Thus, clinical procedures of sample preparation should be furthermore optimised and standardised as new standards of operation performance for clinical spatial transcriptome. Our data suggested that the temporary preservation time and condition of samples at operation room should be within 30 min and in 'dry' status. The direct cryo-preservation within OCT media for human lung sample is recommended. Thus, we believe that clinical spatial transcriptome will be a decisive approach and bridge in the development of spatiotemporal molecular images and provide new insights for understanding molecular mechanisms of diseases at multi-orientations.
Keyphrases
- single cell
- gene expression
- rna seq
- genome wide
- deep learning
- optical coherence tomography
- convolutional neural network
- endothelial cells
- dna methylation
- squamous cell carcinoma
- high resolution
- social media
- machine learning
- diabetic retinopathy
- papillary thyroid
- case report
- single molecule
- human health
- health information
- data analysis