ISO/TS 24932:2026

• Abstract:

  This document specifies procedures for gene expression-based similarity calculation between human pluripotent stem cell (hPSC)-derived organoids and a pre-defined data set of gene expression profiles in normal tissues. This document covers situations where the gene expression in the organoids have been quantified in a way functionally similar to the samples in the pre-defined dataset, and it is not intended for medical decisions.

• Status: Published
• Publication Date: 05-Aug-2026
• ICS: 35.240.80 - IT applications in health care technology
• Technical Committee: ISO/TC 215/SC 1 - Genomics Informatics
• Drafting Committee: ISO/TC 215/SC 1 - Genomics Informatics
• Current Stage: 6060 - International Standard published
• Start Date: 06-Aug-2026
• Due Date: 29-Aug-2027
• Completion Date: 06-Aug-2026

Overview

ISO/TS 24932: Genomics Informatics - Procedures for Gene Expression Panel-Based Similarity Calculation for Human Pluripotent Stem Cell-Derived Organoids provides standardized guidance for assessing the similarity between human pluripotent stem cell (hPSC)-derived organoids and their corresponding human target organs. Focusing on transcriptome-based, quantitative calculations, this technical specification enables researchers to evaluate the maturation and functional fidelity of organoids by comparing gene expression profiles against pre-defined datasets representative of normal tissues.

ISO/TS 24932 is designed for research and quality assessment contexts, and is not intended to guide clinical or medical decisions. The procedures outlined enable reproducible, quantitative analysis, supporting the advancement of organoid research, development, and characterization.

Key Topics

• Gene Expression Similarity Calculation: Methods for comparing gene expression profiles of hPSC-derived organoids with normal tissue references, quantifying how closely an organoid resembles its target organ.
• Panel Configuration: Guidance on selecting and processing gene datasets (e.g., from resources like the GTEx public database), filtering inappropriate genes (e.g., sex-specific or blood cell genes), and establishing organ-specific gene expression panels.
• Normalization and Quality Control: Recommendations for data normalization (such as TPM transformation and log2 scaling) and minimum quality thresholds for RNA-seq data, ensuring consistency and reliability in analyses.
• Algorithm Construction: Instructions for constructing and applying algorithms based on statistical criteria, including managing non-expressed genes, normalization of gene expression values, and handling data from both undifferentiated cells and target organs.
• Data Processing Procedures:
  • Dataset preparation and version tracking
  • Exclusion criteria to avoid false positives
  • Log2 normalization and identification of differentially expressed genes through statistical testing
  • Use of Manhattan distance-based similarity scoring

Applications

ISO/TS 24932 is valuable in a range of genomics informatics workflows and stem cell research applications, such as:

• Quality Control of Organoids: Offering a reproducible, quantitative method to evaluate differentiation or maturation status, supporting research and development of human organoid models.
• Comparative Analysis: Facilitating direct comparison of organoids produced using varied protocols, platforms, or in different laboratories by applying standardized procedures for transcriptomic similarity assessment.
• Method Validation: Providing a reference methodology for characterizing the fidelity of hPSC-derived organoids before downstream biological or pharmacological studies.
• Panel Development: Enabling laboratories to develop or refine customized, organ-specific gene expression panels based on publicly available datasets, increasing the relevance and accuracy of their analyses.
• Bioinformatics Tool Integration: The similarity calculation framework can be embedded in web-based analytics systems or incorporated into bioinformatics pipelines, supporting high-throughput organoid characterization.

Related Standards

Several ISO standards and databases are referenced within ISO/TS 24932 to ensure methodological rigor and terminological consistency:

• ISO 21474-1:2020: Terminology and requirements for nucleic acid quality evaluation in multiplex molecular testing.
• ISO/TS 22690:2021: Criteria for reliability assessment of high-throughput gene expression data.
• ISO 24603:2022: Requirements for human and mouse pluripotent stem cells in biobanking.
• GTEx Portal: A key public resource for gene expression profiles across normal human tissues (https://gtexportal.org).
• Web-based Similarity Analysis System (W-SAS): An implementation example for algorithmic similarity analysis (https://www.kobic.re.kr/wsas/).
• Related Publications: Reference methods and use-cases, such as those described in Nature Communications, further illustrate application of these procedures.

Practical Value

By implementing the recommendations in ISO/TS 24932, researchers, institutions, and developers gain:

• Standardized Quantification: Remove subjectivity from organoid assessment by using consistent, internationally recognized procedures.
• Reproducibility: Enhance confidence in results through detailed reporting and strict quality thresholds for sample preparation, sequencing, and analysis.
• Interoperability: Simplify data sharing and comparison across projects, research groups, and platforms through common data formats, reference panels, and analytic workflows.
• Enhanced Research Impact: Support regulatory submission, publication, or collaborative research with robust, traceable similarity metrics for stem cell-derived organoids.

Adopting ISO/TS 24932 fosters best practices in genomics informatics, builds trust in organoid-based research, and accelerates the development of next-generation biomedical models.

Technical
Specification
ISO/TS 24932
First edition
Genomics informatics —
2026-08
Procedures for gene expression
panel-based similarity calculation
for human pluripotent stem cell-
derived organoids
Informatique génomique — Procédures de calcul de similarité
fondées sur des panels d’expression génique pour les organoïdes
dérivés de cellules souches pluripotentes humaines
Reference number
© ISO 2026
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
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or ISO’s member body in the country of the requester.
ISO copyright office
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Phone: [phone]
Email: [e-mail]
Website: www.iso.org
Published in Switzerland
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Procedures for gene data processing . 3
4.1 Configuring data set from publicly available gene data source .3
4.2 Avoiding false-positive results .3
4.3 Configuring an organ specific gene expression panel .3
4.4 Construction of the algorithm method .5
4.5 RNA-seq production method .6
4.5.1 RNA-seq result generation .6
4.5.2 Minimum quality requirements for RNA-Seq data generation .6
4.5.3 Information management.6
4.6 RNA-seq result conversion method .6
Annex A (informative) Quantitative stomach similarity calculation for stomach organoid . 8
Bibliography .12

iii
Foreword
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This document was prepared by Technical Committee ISO/TC 215, Health informatics, Subcommittee SC 1,
Genomics and Multi-Omics Informatics.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html.

iv
Introduction
An organoid is a self-organized three-dimensional tissue that is typically derived from stem cells, and which
recapitulates the key functional and physiological complexity of an organ. Organoids mimic the cellular
heterogeneity, functionality, architecture and molecular queue of the organ or diseased tissue from which
they are derived.
Until now, to verify the similarity of organoids, the expression of organ-specific proteins or the activity
of enzymes was tested to qualitatively evaluate the function of specific organs. However, it was difficult
to quantitatively evaluate the similarity of various organoids produced in each laboratory because the
criteria were different. The Korea Research Institute of Bioscience and Biotechnology (KRIBB) developed a
quantitative prediction system to assess the similarity (percentage) of human pluripotent stem cell (hPSC)-
derived organoids to the target organ, to improve conventional qualitative similarity assessment.
This document provides requirements and recommendations for developing a transcriptome-based
quantitative calculation method for a simple and reproducible analysis of hPSC-derived human organoids
similarity, and incorporates organ-specific characteristics to assess the differentiation level of hPSC-
derived human organoids, enabling a simple and reproducible analysis of their similarity. In this context,
the proposed method focuses on calculating organ-specific similarity (%) between hPSC-derived organoids
and their corresponding target organs. This similarity serves as a quantitative indicator of organoid quality,
where a higher similarity reflects a higher degree of maturation and functional fidelity to the target organ.
The quantitative calculation systems to assess organ-specific similarity based on Organ-specific Gene
[5]
Expression Panels (Organ-GEP) utilize the Genotype Tissue Expression (GTEx) public database and Organ-
GEP-based calculation algorithms, including a lung-specific gene expression panel (LuGEP), a stomach-
specific gene expression panel (StGEP), and a heart-specific gene expression panel (HtGEP). GTEx is a large-
scale database that analyses gene expression across multiple organs from healthy individuals of various age
groups, sex and ethnicities. It is used to understand the functional characteristics, developmental processes,
and disease mechanisms of each organ. Using this document, it is possible to create specific panels for
various human organs, which can be used to calculate the quantitative organ-specific similarity for each
hPSC-derived organoid. A specific example has been provided in Annex A.
[6]
Based on the guidance and requirements of this document, a web-based similarity analytics system could
be established to provide an analytical algorithm to calculate similarity (percentage) and gene expression
patterns for direct comparison with cells differentiated into specific lineages using human target organ
gene panels (e.g. liver, lung, stomach, heart), providing researchers with valuable information for generating
high-similarity organoids (Figure 1).

v
Figure 1 — Schematic overview of a similarity calculation system

vi
Technical Specification ISO/TS 24932:2026(en)
Genomics informatics — Procedures for gene expression
panel-based similarity calculation for human pluripotent
stem cell-derived organoids
1 Scope
This document specifies procedures for gene expression-based similarity calculation between human
pluripotent stem cell (hPSC)-derived organoids and a pre-defined data set of gene expression profiles in
normal tissues. This document covers situations where the gene expression in the organoids have been
quantified in a way functionally similar to the samples in the pre-defined dataset, and it is not intended for
medical decisions.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
algorithm
set of rules or calculations applied to test data that generate an interpretable or reportable result
[SOURCE: ISO 21474-1:2020, 3.2]
3.2
gene
specific sequence of nucleotides located on a chromosome as a functional unit of inheritance transferred
from a parent to offspring
[SOURCE: ISO/TS 22690:2021, 3.7]
3.3
gene expression
process by which information from a gene (3.2) is used in the synthesis of a functional gene product
[SOURCE: ISO/TS 22690:2021, 3.9]
3.4
induced pluripotent stem cell
iPSC
pluripotent stem cell (3.7) that is generated from somatic cells through artificial reprogramming by the
introduction of genes (3.2) or proteins, or via chemical or drug treatment
[SOURCE: ISO 24603:2022, 3.17]

3.5
next generation sequencing
NGS
device capable of reading nucleotide sequences of huge numbers of genes (3.2) at high speed
[SOURCE: ISO 23732:2021, 3.3]
3.6
human PSC-derived organoid
hPSC-derived organoid
multicellular unit derived from human pluripotent stem cells (3.7) that form 3D structures to simulate a
native organ/tissue development, functions and structure
3.7
pluripotent stem cell
PSC
stem cell that can differentiate into all cell types of the body and is able to self-renew indefinitely in vitro
Note 1 to entry: PSCs include embryonic stem cells (ESCs) (including fertilization derived ESCs, somatic cell nuclear-
transferred stem cells, etc.) and induced pluripotent stem cell (iPSCs) (3.4).
Note 2 to entry: ESC-like cells can also be isolated by parthenogenetic division of oocytes or other haploid cell sources,
and these cells have many of the characteristics of ESCs. However, certain features of these pluripotent cell types can
require specific characterization approaches.
[SOURCE: ISO 24603:2022, 3.21]
3.8
RNA-sequence
RNA-seq
high-throughput sequencing technology to reveal the presence and quantity of RNA molecules in a biological
sample at a given moment in time
[SOURCE: ISO/TS 22690:2021, 3.18]
3.9
transcriptome
set of all RNA molecules in one cell or a population of cells for a specific developmental stage or physiological
condition
[SOURCE: ISO/TS 22690:2021, 3.21]
3.10
phred score
quality score indicating the accuracy of base calls in RNA sequencing, as in DNA sequencing
Note 1 to entry: It represents the reliability of each base call in RNA fragments.
3.11
adaptor
synthetic DNA sequence ligated to both ends of RNA fragments during library preparation, enabling
recognition, amplification, and sequencing by the platform
3.12
pre-defined dataset
dataset of gene-expression profiles from a broad range of tissue types
[5]
EXAMPLE GTEx
3.13
fragments per kilobase of transcript per million mapped reads
FPKM
normalized measure of RNA expression for paired-end sequencing data

3.14
reads per kilobase of transcript per million mapped reads
RPKM
normalized measure of RNA expression for single-end sequencing data
3.15
transcripts per million
TPM
normalized measure of RNA expression that represents the relative abundance of each transcript among all
transcripts in a sample
4 Procedures for gene data processing
4.1 Configuring data set from publicly available gene data source
To evaluate the similarity between the target organ with the organoid specific panel, the use of gene
expression data should generally include the following:
a) Gene expression data (RNA-seq) for each organ dataset should be obtained from publicly available gene
expression database (DB).
b) Evaluators should keep a record of the version information of the public DB for tracking.
c) The gene data should be assessed by the evaluator for the purpose of similarity evaluation.
d) The gene data should be selected to reflect the characteristics and functions of each tissue.
4.2 Avoiding false-positive results
To avoid false positive results, the user shall:
a) exclude the sex-specific tissues: ovary, uterus, vagina, fallopian tube, testis and cervix;
b) exclude blood cells: whole blood and blood cells.
4.3 Configuring an organ specific gene expression panel
When configuring an organ specific gene panel:
a) Gene expression datasets contain all tissues from the GTEx portal and the genes shall be limited to
protein-coding genes.
b) Transcription activity should be considered above a minimal threshold FPKM/RPKM/TPM ≥ 1 for most
tissues.
c) Transcriptional activity within a chromosome should be defined as the FPKM/RPKM/TPM of expressed
genes on that chromosome.
d) Non-expressed genes and/or genes with extremely low expression shall be filtered out.
e) Gene sets of tissues shall be matched to compare the gene expression differences between tissues.
f) Specific protein-coding genes shall be extracted from the Ensembl™ gene ID information provided by
the public RNA-seq data.
g)
...