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Record W4405046742 · doi:10.1182/blood-2024-210038

Don't Fly and Freeze: Impact of Ex-Vivo-Time, Cell Concentration and Cryopreservation on the Composition of Allogeneic Stem Cell Grafts

2024· article· en· W4405046742 on OpenAlexaff
Igor Novitzky‐Basso, Emily Fu, M.C Barroso Alvarez, Caden Chiarello, Mohammed Kawari, Megan Nelles, Alexander Marks de Chabris, Ronal Ramos de Armas, Melissa Kissoon, Mark Camacho, Orlay Lopez-Perez, Tulsidai Ramdass, Saqeeful Haque, Valerie McKay, Muhammad Badawi, Rashied Kawsher Molla, Rupal Hatkar, Rachel Jihye Kim, Monoleena Khan, Bramdeo Motiram, Lynn Jean, Mónica Chacón, Amina L. Mohamed, Madhavi Gerbitz, Abdullah Siddique, Miyada Himmat, Ahmed Najemeldin, Lydia Morrison, Mats Remberger, Jonas Mattsson, Armin Gerbitz

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsCryopreservationLeukapheresisAndrologyStem cellViability assayApheresisBiologyEx vivoImmunologyTransplantationCD34MedicineCellIn vivoInternal medicineBiotechnologyCell biologyBiochemistryPlatelet

Abstract

fetched live from OpenAlex

Background CD34+ cells are considered the active substance in allogenic hematopoietic stem cell (aHSC) grafts. However, immunologically active cellular “contaminants” make up >90% of nucleated cells in aHSC grafts, and are crucial for treatment success in hematological disorders. Following COVID-19 pandemic restrictions that were implemented due to supply chain interruptions and infection risk concerns, we conducted a prospective quality analysis on cell composition and viability before and after cryopreservation in aHSC grafts. Methods We analyzed fresh grafts from matched related (n=192) and unrelated (MUD; n=263) donors, and cryopreserved grafts (related, n=34; unrelated, n=97) between April 2020 and December 2023; cell composition, concentration, and time spent ex-vivo (apheresis to infusion/cryopreservation) were recorded. Flow cytometric analysis was carried out upon receipt at our institution and after thaw (if cryopreserved). Viability and absolute counts were studied in CD34+ stem cells, monocytes (CD14+), T cells (CD3+, CD4+ and CD8+), B cells (CD19+), and NK cells (CD56+). Grafts were received from 22 countries (grouped into 3 regions: Asia, Europe, Americas); many were also obtained through local apheresis (all related donors). Results Upon arrival, CD34+ cell viability was 99.0% (82-100%), with the bottom 10% of MUD grafts showing low average viability of 88.1%. Overall CD3+ cells exhibited high viability of 98.3% (59.1-100), with MUD grafts largely contributing to the lowest values; 29 had CD3+ viability <93.7%. Viability of CD4+ cells ranged much wider than CD8+ cells (54.4-100% and 68.3-100%, respectively). Given these results, we analyzed the impact of ex-vivo time on viability. Locally acquired grafts (n=225) had short ex-vivo times (15.5h±11.0) compared to grafts from abroad (Asia, n=5, 59.7h±20.9; Europe, n=270, 44.2h±11.1; Americas, n=84, 27.0h±11.1). The effect of ex-vivo time on viability was significant for a cell types (p<0.001). As expected, there was a limited inverse correlation in CD34+ cells (R2=0.038). However, a strong inverse correlation was found in T cells (CD3+ cells R2=0.167, CD4+ R2=0.168, CD8+ R2=0.148, TCRα/β R2=0.172, TCRg/d, R2=0.044). The effect on viability was minimal in CD56+, 14+, and 19+ cells (all >99.5%, 92-100). Subsequently, we reasoned that cell concentration in the apheresis bag may equally impact viability. While CD34+ viability (R2=0.002, p=0.969) showed no correlation, CD45+ viability was significantly positively affected (R2=0.013, p=0.004). No correlation of viability with cell concentration was seen in CD14+ cells (R2=0.001, p=0.672), CD19+ cells (R2=0.002, p=0.755) and CD56+ cells (R2=0.000, p=0.293), but nearly all T cell subsets were positively affected (CD3+ R2=0.020, CD4+ R2=0.020, CD8+ R2=0.021, TCRα/β R2=0.021, all p<0.001, TCRg/d R2=0.002, p=0.159). Cell concentrations were higher in local products compared to MUD (p<0.001), prompting us to create an index to elucidate the relationship of both factors (PMIndex=Ex-Vivo time x cell concentration). PMindex was significantly inversely correlated with viability of all cell subsets (p<0.001), except CD19+ cells (p=0.409). Viability correlation was weak for CD34+ (R2=0.047), CD14+ (R2=0.121), and CD56+ (R2=0.069) cells. Viability was most affected in T cells when ex-vivo time was long and cell concentration was high: CD3+ R2=0.125, CD4+ R2=0.127, CD8+ R2=0.107, TCRα/β R2=0.129, TCRg/d R2=0.037 (all p<0.001). When grafts were analyzed post cryopreservation (n=131), high PMIndex did not impact CD34+ (p=0.944), CD14+ (p=0.148), CD19+ (p=0.054), and CD56+ (p=0.064) cells, while T cells were impacted substantially (p<0.001 for CD3+, 4+, 8+, and TCRα/β; TCRg/d p=0.121). Thus, cryopreservation significantly reduced the number of viable T cells infused (CD3+=-47.6%, CD4+=-52.1%, CD8+=-38.6%, TCRα/β=-48.0%, TCRg/d=-35.6%). CD34+ (-8.3%), CD14+ (-27.1%), CD19+ (-16.7%) and CD56+ (-21.3%) cells were only mildly affected. Conclusion All aHSC graft constituents are affected by ex-vivo time, cell concentration and cryopreservation; CD34+ cells are the most tolerant and T cells the most affected. The data presented may have clinical implications for patients receiving cryopreserved grafts with long ex-vivo times and high cell concentrations. More granular analysis of graft composition should be performed when cryopreservation is planned.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.246
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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