ArticleCurrent opinion in biomedical engineering2023
Touch-free optical technologies to streamline the production of T cell therapies.
Article in Current opinion in biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Redox index and capacity analysis (RICA) reveals NAD biology changes in T cell responses in vitro and ex vivo.Journal of immunology (Baltimore, Md. : 1950) · 2026Article
- Label-free metabolic imaging monitors the fitness of chimeric antigen receptor T cells.Nature biomedical engineering · 2026Article
- Capacitance Technology Enables Automated Feeding, Improved Expansion, and Higher Throughput of CAR-T Cell Stirred-Tank Bioreactor Cultures.Biotechnology journal · 2026Article
- Real-time, high-resolution metabolic characterization of live bacteria using label-free optical metabolic imaging.NPJ biofilms and microbiomes · 2026Article
- Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.Journal of biomedical optics · 2025Article
- Tumour organoids and assembloids: Patient-derived cancer avatars for immunotherapy.Clinical and translational medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Currently approved adoptive T cell therapy relies on autologous (obtained from the same patient) T cells, which often suffer from poor quality that diminishes treatment efficacy. Due to the heterogeneous nature of T cell quality between and within patients, significant efforts are aimed at optimizing cell manipulation and growth conditions for potent T cell products. We believe that touch-free imaging and sensing technologies are critical to monitor single-cell features during T cell manufacturing to ensure consistent and optimally timed methods for cell manipulation and growth. Here, we discuss emerging label-free optical imaging and sensing methods, along with machine learning techniques that could enable in-line feedback to optimize T cell quality at multiple stages during manufacturing. These methods have the potential to streamline current workflow, accelerate the manufacture of safe high-quality T cell therapies, and improve our understanding of the dynamic, heterogeneous processes of T cell manufacturing.
Identifiers
What OpenQuestion holds
Registered trials
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.