Evidence map›Paper›PMID 40821673›Full record

ArticleFrontiers in bioengineering and biotechnology2025

Automated, aseptic sampling with small-volume capacity from microbioreactors for cell therapy process analysis.

Zhi Xian Chan, Shruthi Pandi Chelvam, Wei-Xiang Sin, Denise Bei Lin Teo, Ahmad Amirul Bin Abdul Rahim, Ying Ying Wu, Dan Liu, Michael E Birnbaum, Derrick Yong, Rajeev J Ram

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2025. 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
–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

1 citing paper in PubMed.

  1. Bioreactor Design and Engineering for Cultivated Meat Manufacturing.Advances in biochemical engineering/biotechnology · 2026
    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

10 authors.

Zhi Xian ChanCritical Analytics for Manufacturing Personalized Medicine, Singapore MIT Alliance for Research and Technology Centre, Singapore, Singapore.
Shruthi Pandi ChelvamCritical Analytics for Manufacturing Personalized Medicine, Singapore MIT Alliance for Research and Technology Centre, Singapore, Singapore.
Wei-Xiang SinCritical Analytics for Manufacturing Personalized Medicine, Singapore MIT Alliance for Research and Technology Centre, Singapore, Singapore.
Denise Bei Lin TeoCritical Analytics for Manufacturing Personalized Medicine, Singapore MIT Alliance for Research and Technology Centre, Singapore, Singapore.
Ahmad Amirul Bin Abdul RahimBiomanufacturing Technology, Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Ying Ying WuBiomanufacturing Technology, Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Dan LiuBiomanufacturing Technology, Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Michael E BirnbaumCritical Analytics for Manufacturing Personalized Medicine, Singapore MIT Alliance for Research and Technology Centre, Singapore, Singapore.
Derrick YongCritical Analytics for Manufacturing Personalized Medicine, Singapore MIT Alliance for Research and Technology Centre, Singapore, Singapore.
Rajeev J RamCritical Analytics for Manufacturing Personalized Medicine, Singapore MIT Alliance for Research and Technology Centre, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current workflows in autologous cell therapy manufacturing are reliant on manual processes that are difficult to scale out to meet patient demands. High throughput bioreactor systems that enable multiple cultures to occur in parallel can address this need, but require good bioprocess monitoring workflows to produce good quality cell therapy products. Commercial sampling systems have thus been developed for better feedback control and monitoring capabilities. However, they are targeted towards large scale processes and often bioreactor specific, making them less robust for integration across different bioreactor scales and types, such as perfusion-capable microbioreactors which allows for greater process intensification. Here, an automated cell culture sampling system (Auto-CeSS) was developed to eliminate laborious manual sampling while minimizing sterility risks for cell therapy manufacturing processes. The system is aseptically integrated with a variety of bioreactors of different working volumes. This system can accurately and aseptically sample a minimum volume of 30 μL and can consistently perform periodic sampling of supernatant over a minimum interval of 15 min. We integrated Auto-CeSS with a 2 mL perfusion microbioreactor and a 8 mL gas-permeable well-plate for T cell culture, collecting 200 μL of supernatant samples daily for metabolite analysis. Comparison of the metabolic profiles of the samples collected via Auto-CeSS versus manual sampling revealed insignificant differences in metabolite levels, including glucose, lactate, glutamine, and glutamate. This report demonstrates the potential of Auto-CeSS as an at-line sampling platform in a real-time T cell production run to facilitate in-process culture monitoring.

Indexed as

asepticautosamplerbioreactorcar-tmicrobioreactorsamplingsmall-volume

Identifiers

PMID40821673
PMCPMC12351187

What OpenQuestion 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 OpenQuestion graph.