Evidence map›Paper›PMID 33377132›Full record

ArticleCell reports. Medicine2020

An Automated Organoid Platform with Inter-organoid Homogeneity and Inter-patient Heterogeneity.

Shengwei Jiang, Haoran Zhao, Weijie Zhang, Jiaqi Wang, Yuhong Liu, Yuanxiong Cao, Honghui Zheng, Zhiwei Hu, Shubin Wang, Yu Zhu and 5 more

Open access · goldAbstract read
In one paragraph

Article in Cell reports. Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
105citing papers in PubMed, 1 pooled it
5.6field-weighted citation impact, top 3% of its field
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

105 citing papers in PubMed, 1 synthesis or guideline pooled it, 165 citations in OpenAlex.

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  16. Organoid Brain-Machine-Interface Devices for Central Nervous System Repair.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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45 more citing papers are in PubMed but not listed here.

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

15 authors at 6 institutions in 1 country.

Shengwei JiangTsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Shenzhen Key Laboratory of Gene and Antibody Therapy, State Key Laboratory of Chemical Oncogenomics, Tsinghua University, Shenzhen 518055, China.
Haoran ZhaoTsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Shenzhen Key Laboratory of Gene and Antibody Therapy, State Key Laboratory of Chemical Oncogenomics, Tsinghua University, Shenzhen 518055, China.
Weijie ZhangDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, No1 Jianshe East Road, Zhengzhou 450052, China.
Jiaqi WangTsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Shenzhen Key Laboratory of Gene and Antibody Therapy, State Key Laboratory of Chemical Oncogenomics, Tsinghua University, Shenzhen 518055, China.
Yuhong LiuDepartment of Chemistry, Tsinghua University, Beijing 100084, China.
Yuanxiong CaoTsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Shenzhen Key Laboratory of Gene and Antibody Therapy, State Key Laboratory of Chemical Oncogenomics, Tsinghua University, Shenzhen 518055, China.
Honghui ZhengTsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Shenzhen Key Laboratory of Gene and Antibody Therapy, State Key Laboratory of Chemical Oncogenomics, Tsinghua University, Shenzhen 518055, China.
Zhiwei HuTsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Shenzhen Key Laboratory of Gene and Antibody Therapy, State Key Laboratory of Chemical Oncogenomics, Tsinghua University, Shenzhen 518055, China.
Shubin WangDepartment of Oncology, Peking University Shenzhen Hospital, Shenzhen Key Laboratory of Gastrointestinal Cancer Translational Research, Cancer Institute of Shenzhen PKU-HKUST Medical Center, 1120 Lianhua Road, Shenzhen 518036, China.
Yu ZhuDepartment of Oncology, Peking University Shenzhen Hospital, Shenzhen Key Laboratory of Gastrointestinal Cancer Translational Research, Cancer Institute of Shenzhen PKU-HKUST Medical Center, 1120 Lianhua Road, Shenzhen 518036, China.
Wei WangDepartment of Pathology, Shenzhen University General Hospital, Shenzhen 518055, China.
Shuzhong CuiDepartment of Abdominal Surgery, Affiliated Cancer Hospital of Guangzhou Medical University, Guangzhou 510095, China.
Peter E LobieTsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Shenzhen Key Laboratory of Gene and Antibody Therapy, State Key Laboratory of Chemical Oncogenomics, Tsinghua University, Shenzhen 518055, China.
Laiqiang HuangTsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Shenzhen Key Laboratory of Gene and Antibody Therapy, State Key Laboratory of Chemical Oncogenomics, Tsinghua University, Shenzhen 518055, China.
Shaohua MaTsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Shenzhen Key Laboratory of Gene and Antibody Therapy, State Key Laboratory of Chemical Oncogenomics, Tsinghua University, Shenzhen 518055, China.
Tsinghua–Berkeley Shenzhen Institute · CNPeking University Shenzhen Hospital · CNFirst Affiliated Hospital of Zhengzhou University · CNGuangzhou Medical University · CNShenzhen University · CNTsinghua University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current organoid technologies require intensive manual manipulation and lack uniformity in organoid size and cell composition. We present here an automated organoid platform that generates uniform organoid precursors in high-throughput. This is achieved by templating from monodisperse Matrigel droplets and sequentially delivering them into wells using a synchronized microfluidic droplet printer. Each droplet encapsulates a certain number of cells (e.g., 1,500 cells), which statistically represent the heterogeneous cell population in a tumor section. The system produces >400-μm organoids within 1 week with both inter-organoid homogeneity and inter-patient heterogeneity. This enables automated organoid printing to obtain one organoid per well. The organoids recapitulate 97% gene mutations in the parental tumor and reflect the patient-to-patient variation in drug response and sensitivity, from which we obtained more than 80% accuracy among the 21 patients investigated. This organoid platform is anticipated to fulfill the personalized medicine goal of 1-week high-throughput screening for cancer patients.

Indexed as

AutomationPrecision MedicineAnimalsBiometryHigh-Throughput Screening AssaysHumansMiceMicrofluidicsNeoplasmsOrganoidsPrinting, Three-Dimensionaldropletmicrofluidicsorganoidprintingtumor

Identifiers

PMID33377132
PMCPMC7762778
OpenAlexW3115787212

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.