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Record W2620614367 · doi:10.1016/j.omtm.2017.05.007

Lentiviral Fluorescent Genetic Barcoding for Multiplex Fate Tracking of Leukemic Cells

2017· article· en· W2620614367 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMolecular Therapy — Methods & Clinical Development · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersCanadian Institutes of Health ResearchDeutsche KrebshilfeUniversity of British ColumbiaBC Cancer AgencyFaculty of Medicine, University of British Columbia
KeywordsMultiplexBiologyLeukemiaHoming (biology)In vivoComputational biologyCancer researchImmunologyBioinformaticsGenetics

Abstract

fetched live from OpenAlex

Tracking the behavior of leukemic samples both in vitro and in vivo plays an increasingly large role in efforts to better understand the leukemogenic processes and the effects of potential new therapies. Such work can be accelerated and made more efficient by methodologies enabling the characterization of leukemia samples in multiplex assays. We recently developed three sets of lentiviral fluorescent genetic barcoding (FGB) vectors that create 26, 14, and 6 unique immunophenotyping-compatible color codes from GFP-, yellow fluorescent protein (YFP)-, and monomeric kusabira orange 2 (mKO2)-derived fluorescent proteins. These vectors allow for labeling and tracking of individual color-coded cell populations in mixed samples by real-time flow cytometry. Using the prototypical Hoxa9/Meis1 murine model of acute myeloid leukemia, we describe the application of the 6xFGB vector system for assessing leukemic cell characteristics in multiplex assays. By transplanting color-coded cell mixes, we investigated the competitive growth behavior of individual color-coded populations, determined leukemia-initiating cell frequencies, and assessed the dose-dependent potential of cells exposed to the histone deacetylase inhibitor Entinostat for bone marrow homing. Thus, FGB provides a useful tool for the multiplex characterization of leukemia samples in a wide variety of applications with a concomitant reduction in workload, processing times, and mouse utilization. Tracking the behavior of leukemic samples both in vitro and in vivo plays an increasingly large role in efforts to better understand the leukemogenic processes and the effects of potential new therapies. Such work can be accelerated and made more efficient by methodologies enabling the characterization of leukemia samples in multiplex assays. We recently developed three sets of lentiviral fluorescent genetic barcoding (FGB) vectors that create 26, 14, and 6 unique immunophenotyping-compatible color codes from GFP-, yellow fluorescent protein (YFP)-, and monomeric kusabira orange 2 (mKO2)-derived fluorescent proteins. These vectors allow for labeling and tracking of individual color-coded cell populations in mixed samples by real-time flow cytometry. Using the prototypical Hoxa9/Meis1 murine model of acute myeloid leukemia, we describe the application of the 6xFGB vector system for assessing leukemic cell characteristics in multiplex assays. By transplanting color-coded cell mixes, we investigated the competitive growth behavior of individual color-coded populations, determined leukemia-initiating cell frequencies, and assessed the dose-dependent potential of cells exposed to the histone deacetylase inhibitor Entinostat for bone marrow homing. Thus, FGB provides a useful tool for the multiplex characterization of leukemia samples in a wide variety of applications with a concomitant reduction in workload, processing times, and mouse utilization.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.312
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.138
GPT teacher head0.432
Teacher spread0.294 · 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