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

ISO/TS 24932:2026 is an ISO Technical Specification from ISO on genomics informatics. It specifies procedures for calculating gene expression panel-based similarity between human pluripotent stem cell (hPSC)-derived organoids and a pre-defined dataset of normal tissue gene-expression profiles. The document is for similarity analysis and quality assessment of organoids, not for medical decisions.

What does ISO/TS 24932:2026 specify?

ISO/TS 24932:2026 specifies a workflow for comparing hPSC-derived organoids with reference gene-expression profiles from normal tissues. It covers how to build the reference data set, how to select organ-specific genes, how to produce and normalize RNA-seq data, and how to calculate a similarity score.

Annex	What it covers
Annex A (informative)	A stomach-specific example of quantitative similarity calculation for a stomach organoid

Clause 4 contains the technical procedure. It is organized into data set configuration, false-positive avoidance, organ-specific panel construction, algorithm construction, RNA-seq production, quality requirements, information management, and RNA-seq result conversion.

What are the key requirements of ISO/TS 24932:2026?

Reference data and tissue selection

Clause 4.1 says the reference data should come from publicly available gene-expression databases, and the database version should be recorded. In practice, this gives traceability, so the same panel can be reviewed or repeated later.

Clause 4.2 requires exclusion of sex-specific tissues and blood-related tissues to avoid false-positive similarity results. That matters because those tissues can distort organ-specific gene selection.

Building an organ-specific panel

Clause 4.3 limits the panel to protein-coding genes and filters out non-expressed or very low-expression genes. It uses normalized expression values, tissue-to-tissue comparison, t-tests, and confidence intervals to keep only genes that are specific to the target tissue.

In practical terms, the panel should contain genes that separate one organ from the others, not genes that are broadly active everywhere. The clause also points to the use of log2-normalized values and a statistical selection rule based on tissue-specific expression boundaries.

Calculating similarity

Clause 4.4 defines the scoring approach. The organoid data are filtered to the target tissue-specific genes, compared with undifferentiated cell profiles such as human embryonic stem cell (hESC) or induced pluripotent stem cell (iPSC) data, and then converted to a percentage score.

The method uses boundary values for the target tissue and undifferentiated cells, clips values outside those boundaries, and then calculates distance with the Manhattan distance. In practice, a higher percentage means the organoid profile is closer to the target organ profile.

RNA-seq production, quality, and conversion

Clause 4.5 requires RNA-seq generation to be functionally equivalent to the pre-defined dataset and allows Illumina or MGI sequencing platforms. It sets minimum quality expectations, including RNA integrity number (RIN) of 7 or higher, Q30 or higher reads, and more than 5 Gbp per sample, which corresponds to at least 50 million reads.

Clause 4.5.3 also asks users to keep records of the instrument model, read length, and software used for quality control, low-quality read filtering, adaptor filtering, and expression calculation. That helps reproduce the analysis and compare results across laboratories.

Clause 4.6 converts FPKM or RPKM values to transcripts per million (TPM), adds 1, transforms the values with log2, and then calculates similarity using a statistical programming language. In practice, the document standardizes the comparison step so different data sets can be processed in a comparable way.

What terms does ISO/TS 24932:2026 define?

• Algorithm - a set of rules or calculations applied to test data to produce an interpretable or reportable result.
• Gene expression - the process by which information from a gene is used to synthesize a functional gene product.
• Human PSC-derived organoid - a multicellular unit derived from human pluripotent stem cells that forms 3D structures simulating native organ/tissue development, functions and structure.
• Pre-defined dataset - a dataset of gene-expression profiles from a broad range of tissue types, with GTEx given as the example.
• RNA-seq - high-throughput sequencing technology used to reveal the presence and quantity of RNA molecules in a biological sample at a given time.
• TPM - a normalized measure of RNA expression that represents the relative abundance of each transcript among all transcripts in a sample.
• Phred score - a quality score indicating the accuracy of base calls in RNA sequencing.

Who uses ISO/TS 24932:2026?

ISO/TS 24932:2026 is used by genomics informatics teams, organoid researchers, stem cell laboratories, and bioinformatics developers who need a repeatable way to compare organoids with target tissues. It is also relevant to quality managers and technical buyers who need to check whether a workflow supports transcriptome-based similarity analysis.

In practice, users apply it to build organ-specific gene-expression panels, process RNA-seq data, calculate similarity percentages, and document the analysis pipeline. The document also supports web-based similarity analytics systems for direct comparison of differentiated cells and organoid profiles.

Which standards are used with ISO/TS 24932:2026?

ISO/TS 24932:2026 has no normative references. The bibliography cites ISO 21474-1, ISO/TS 22690 and ISO 24603 as related standards for algorithm terminology, gene-expression data reliability, and pluripotent stem cell terminology.

What does the ISO/TS 24932:2026 document contain?

ISO/TS 24932:2026 contains a foreword, an introduction, the scope, terms and definitions, and a technical clause on gene data processing. The technical clause includes formula-based steps for selecting genes, transforming RNA-seq results, and calculating similarity.

Annex A is an informative worked example for a stomach organoid. It describes transcriptome data acquisition, preprocessing, analytical algorithm construction, RNA-seq generation and analysis, and an example result figure showing stomach similarity.

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)
...