Evidence map›Paper›PMID 35083877›Full record

ArticleClinical and translational medicine2022

Clinical challenges of tissue preparation for spatial transcriptome.

Xiaoxia Liu, Yujia Jiang, Dongli Song, Linlin Zhang, Guang Xu, Rui Hou, Yong Zhang, Jian Chen, Yunfeng Cheng, Longqi Liu and 6 more

Abstract read
In one paragraph

Article in Clinical and translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Advances in Spatial Transcriptomics in Bone.Current osteoporosis reports · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Xiaoxia LiuDepartment of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Zhongshan Hospital of Fudan University, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, China.ORCID 0000-0001-6276-3319
Yujia JiangBGI, Shenzhen, China.
Dongli SongDepartment of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Zhongshan Hospital of Fudan University, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, China.
Linlin ZhangDepartment of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Zhongshan Hospital of Fudan University, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, China.
Guang XuInstitute of Computer Science, Fudan University, Shanghai, China.
Rui HouShanghai Biotechnology Corporation, Shanghai, China.
Yong ZhangDepartment of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Zhongshan Hospital of Fudan University, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, China.
Jian ChenShanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Yunfeng ChengJinshan Hospital Centre for Tumor Diagnosis and Therapy, Fudan University Shanghai Medical College, Shanghai, China.
Longqi LiuBGI, Shenzhen, China.
Xun XuBGI, Shenzhen, China.
Gang ChenDepartment of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.
Duojiao WuDepartment of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Zhongshan Hospital of Fudan University, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, China.ORCID 0000-0003-2562-8825
Tianxiang ChenShanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Ao ChenBGI, Shenzhen, China.
Xiangdong WangDepartment of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Zhongshan Hospital of Fudan University, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, China.ORCID 0000-0002-1278-3343

Funding

Operation Funding of Shanghai Institute of Clinical Bioinformatics and Shanghai Engineering and Technology Center for Artificial Intelligence of Lung and Heart Diseases from Zhongshan HospitalThe National Nature Science Foundation of China 81873409
6 · The paper itself

Abstract

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.

Indexed as

Gene Expression ProfilingHumansSpecimen Handlingclinical challengecritical factorspatial transcriptomicsspatiotemporal molecular imagespatiotemporal molecular medicine

Identifiers

PMID35083877
PMCPMC8792118

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.