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Tracking Normal and Chronic Myeloid Leukemia Progenitor Cell Cycle Kinetics in a Defined Niche Using Live Time Lapse Confocal Imaging with Fucci2BL a Novel Lentiviral Bicistronic Reporter

2015· article· en· W2979422744 on OpenAlexaff
Gabriel Pineda, Kathleen M. Lennon, Nathaniel P. Delos Santos, Florence Lambert-Fliszar, Gennarina L. Riso, Marco A. Marra, Sheldon Morris, Asako Sakaue-Sawano, Atsushi Miyawaki, Catriona Jamieson

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsCanada's Michael Smith Genome Sciences Centre
Fundersnot available
KeywordsBiologyProgenitor cellStem cellCell biologyCell cycleFlow cytometryReporter geneCancer researchCellImmunologyGene expressionGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Malignant reprogramming of progenitors into self-renewing cancer stem cells (CSCs) that have a predisposition for dormancy in protective niches has been implicated in therapeutic resistance of chronic myeloid leukemia (CML) and other CSC-driven malignancies. An unmet medical need for developing therapies that target niche dependent dormant human CSCs provides a compelling rationale for identifying key differences in gene expression at different cell cycle phases between normal and malignant progenitors in a CSC-supportive stromal co-culture system. Currently, few methods exist for quantifying cell cycle kinetics in live human leukemia stem cells (LSC). To date, efficient cell cycle transit time analysis in single live human leukemic progenitors derived from primary patient samples has been hampered by 1) decreased cell viability following transfection or transduction, 2) limited sample size, 3) dormancy of primitive progenitor populations thereby necessitating lentiviral rather than retroviral transduction and 4) increased apoptosis in the absence of a supportive microenvironment. To alleviate these challenges and improve transduction efficiency, we generated Fucci2BL, a lentiviral bicistronic reporter vector. Fucci2BL expresses mVenus-hGem(1/110) fused to mCherry-hCdt1(30/120) by the T2A peptide using an EF1 promoter that generates optimal levels of gene expression in progenitors. Initially, the reporter fidelity was characterized in 293A cells using flow cytometry and time-lapse confocal fluorescence microscopy. Time-lapse confocal fluorescence microscopy revealed normal cell morphology and distinct nuclear staining of either green or red fluorescence depending on the cell cycle stage. Once the fidelity of the Fucci2BL reporter was characterized, differences in gene expression levels between normal and malignant progenitors were analyzed. Whole transcriptome RNA-seq analysis revealed both cell cycle and DNA replication pathways were enriched in chronic phase CP (CML) compared to normal progenitors. Cell cycle kinetics between normal and chronic phase (CML) progenitors co-cultured in a niche were also analyzed using the Fucci2BL reporter. Normal progenitor cells on average transited the cell cycle within 26 hours while CP progenitor cells demonstrated a prolongation of transit through G1. In summary, the Fucci2BL system enables single transduction and single cell cycle tracking as well as gene expression changes in live primary progenitors in response to a niche. This robust lentiviral reporter can reproducibly distinguish cell cycle phases thereby providing an opportunity to quantitatively study the contribution of cell cycle kinetics to single cancer stem cell therapeutic resistance and to relapse. Disclosures Jamieson: J&J: Research Funding; GSK: Research Funding.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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