Evidence map›Paper›PMID 38193136›Full record

ArticlePNAS nexus2024

Beyond 2D: A scalable and highly sensitive method for a comprehensive 3D analysis of kidney biopsy tissue.

Hiroyuki Yamada, Shin-Ichi Makino, Issei Okunaga, Takafumi Miyake, Kanae Yamamoto-Nonaka, Juan Alejandro Oliva Trejo, Takahiro Tominaga, Maulana A Empitu, Ika N Kadariswantiningsih, Aurelien Kerever and 5 more

Abstract read
In one paragraph

Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Review
  2. Review
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.

Hiroyuki YamadaDepartment of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan.ORCID https://orcid.org/0000-0002-5167-3147
Shin-Ichi MakinoDepartment of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan.
Issei OkunagaDepartment of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan.ORCID https://orcid.org/0000-0002-7782-8207
Takafumi MiyakeThe Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan.
Kanae Yamamoto-NonakaThe Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan.
Juan Alejandro Oliva TrejoThe Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan.ORCID https://orcid.org/0000-0003-3054-0684
Takahiro TominagaDepartment of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan.
Maulana A EmpituDepartment of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan.ORCID https://orcid.org/0000-0002-6448-7642
Ika N KadariswantiningsihDepartment of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan.ORCID https://orcid.org/0000-0001-5949-252X
Aurelien KereverResearch Institute for Diseases of Old Age, Graduate School of Medicine, Juntendo University, Tokyo 113-8421, Japan.ORCID https://orcid.org/0000-0003-0980-9896
Akira KomiyaDepartment of Urology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan.ORCID https://orcid.org/0000-0003-4071-377X
Tomohiko IchikawaDepartment of Urology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan.ORCID https://orcid.org/0000-0002-8637-4163
Eri Arikawa-HirasawaResearch Institute for Diseases of Old Age, Graduate School of Medicine, Juntendo University, Tokyo 113-8421, Japan.ORCID https://orcid.org/0000-0003-4959-4113
Motoko YanagitaThe Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan.ORCID https://orcid.org/0000-0002-0339-9008
Katsuhiko AsanumaDepartment of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan.ORCID https://orcid.org/0009-0005-4536-4873

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The spatial organization of various cell populations is critical for the major physiological and pathological processes in the kidneys. Most evaluation of these processes typically comes from a conventional 2D tissue cross-section, visualizing a limited amount of cell organization. Therefore, the 2D analysis of kidney biopsy introduces selection bias. The 2D analysis potentially omits key pathological findings outside a 1- to 10-μm thin-sectioned area and lacks information on tissue organization, especially in a particular irregular structure such as crescentic glomeruli. In this study, we introduce an easy-to-use and scalable method for obtaining high-quality images of molecules of interest in a large tissue volume, enabling a comprehensive evaluation of the 3D organization and cellular composition of kidney tissue, especially the glomerular structure. We show that CUBIC and ScaleS clearing protocols could allow a 3D analysis of the kidney tissues in human and animal models of kidney disease. We also demonstrate that the paraffin-embedded human biopsy specimens previously examined via 2D evaluation could be applicable to 3D analysis, showing a potential utilization of this method in kidney biopsy tissue collected in the past. In summary, the 3D analysis of kidney biopsy provides a more comprehensive analysis and a minimized selection bias than 2D tissue analysis. Additionally, this method enables a quantitative evaluation of particular kidney structures and their surrounding tissues, with the potential utilization from basic science investigation to applied diagnostics in nephrology.

Identifiers

PMID38193136
PMCPMC10772983

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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.