Impacts of transient exposure of human T cells to low oxygen, temperature, pH and nutrient levels relevant to bioprocessing for cell therapy applications
Bibliographic record
Abstract
BACKGROUND: T-cell therapy advances have stimulated the development of bioprocesses to address the specialized needs of cell therapy manufacturing. During concentrated cell washing, the cells are frequently exposed to transiently reduced oxygen, temperature, pH, and nutrient levels. Longer durations of these conditions can be caused by process deviations or, if they are not harmful, be used to ease the scheduling of process stages during experiments as well as manufacturing. METHODS: To avoid unpredictable impacts on T-cell quality during bioprocessing, we measured the influences of such environmental exposures generated by settling 250 million activated human T cells per mL, for up to 6 h at temperatures from 4 to 37°C. RESULTS: The measured glucose concentration decreased to as low as 0.5 mM and the pH to 6, while lactate increased up to 55 mM. The concentrated cell conditions at 37°C resulted in by far the greatest losses in viable cell numbers with, on average, only 58% and 41% of the cells recovered after 3 and 6 h, respectively. Likewise, their subsequent cell expansion cultures were substantially reduced even after only 3 h of exposure, and with decreased percentages of central memory T cells and increased percentages of effector memory and effector T cells. Although under similar environmental conditions at room temperatures, the negative impacts of high cell concentrations were greatly diminished for up to 3 h. At 4°C the transient conditions were less extreme, and the cells well maintained for 6 h. CONCLUSIONS: Overall, when developing processes and devices for T-cell therapy manufacturing that involve concentrated cells, the results of this study indicate that more practically feasible room temperatures can be used for up to 3 h to obtain high viable cell recoveries whereas lower temperatures such as 4°C should be used if there is a need for more prolonged concentrated T-cell conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".