Evidence map›Paper›PMID 36097044›Full record

ArticleScientific reports2022

Semibulk RNA-seq analysis as a convenient method for measuring gene expression statuses in a local cellular environment.

Kyoko Muto, Issei Tsuchiya, Soo Hyeon Kim, Satoi Nagasawa, Mariko Takishita, Koichiro Tsugawa, Hiroaki Saito, Yusuke Komazaki, Toru Torii, Teruo Fujii and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Article
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

13 authors at 3 institutions in 1 country.

Kyoko MutoDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.
Issei TsuchiyaDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.
Soo Hyeon KimInstitute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan.
Satoi NagasawaDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.
Mariko TakishitaDivision of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, 216-8511, Japan.
Koichiro TsugawaDivision of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, 216-8511, Japan.
Hiroaki SaitoInstitute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan.
Yusuke KomazakiHuman Augmentation Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Social Innovation Bldg., 6-2-3 Kashiwanoha, Kashiwa, Chiba, 277-0802, Japan.
Toru ToriiDepartment of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.
Teruo FujiiInstitute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan.
Yutaka SuzukiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. ysuzuki@hgc.jp.
Ayako SuzukiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.
Masahide SekiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.
The University of Tokyo · JPSt. Marianna University School of Medicine · JPNational Institute of Advanced Industrial Science and Technology · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When biologically interpretation of the data obtained from the single-cell RNA sequencing (scRNA-seq) analysis is attempted, additional information on the location of the single cells, behavior of the surrounding cells, and the microenvironment they generate, would be very important. We developed an inexpensive, high throughput application while preserving spatial organization, named "semibulk RNA-seq" (sbRNA-seq). We utilized a microfluidic device specifically designed for the experiments to encapsulate both a barcoded bead and a cell aggregate (a semibulk) into a single droplet. Using sbRNA-seq, we firstly analyzed mouse kidney specimens. In the mouse model, we could associate the pathological information with the gene expression information. We validated the results using spatial transcriptome analysis and found them highly consistent. When we applied the sbRNA-seq analysis to the human breast cancer specimens, we identified spatial interactions between a particular population of immune cells and that of cancer-associated fibroblast cells, which were not precisely represented solely by the single-cell analysis. Semibulk analysis may provide a convenient and versatile method, compared to a standard spatial transcriptome sequencing platform, to associate spatial information with transcriptome information.

Indexed as

Gene Expression ProfilingSingle-Cell AnalysisAnimalsHumansMiceRNA-SeqSequence Analysis, RNATranscriptome

Identifiers

PMID36097044
PMCPMC9468030
OpenAlexW4295249532

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.