PMID- 38474094
OWN - NLM
STAT- MEDLINE
DCOM- 20240314
LR  - 20240315
IS  - 1422-0067 (Electronic)
IS  - 1422-0067 (Linking)
VI  - 25
IP  - 5
DP  - 2024 Feb 29
TI  - Phenotypic Analysis of Hematopoietic Stem and Progenitor Cell Populations in 
      Acute Myeloid Leukemia Based on Spectral Flow Cytometry, a 20-Color Panel, and 
      Unsupervised Learning Algorithms.
LID - 10.3390/ijms25052847 [doi]
LID - 2847
AB  - The analysis of hematopoietic stem and progenitor cell populations (HSPCs) is 
      fundamental in the understanding of normal hematopoiesis as well as in the 
      management of malignant diseases, such as leukemias, and in their diagnosis and 
      follow-up, particularly the measurement of treatment efficiency with the 
      detection of measurable residual disease (MRD). In this study, I designed a 
      20-color flow cytometry panel tailored for the comprehensive analysis of HSPCs 
      using a spectral cytometer. My investigation encompassed the examination of 
      forty-six samples derived from both normal human bone marrows (BMs) and patients 
      with acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) along with 
      those subjected to chemotherapy and BM transplantation. By comparing my findings 
      to those obtained through conventional flow cytometric analyses utilizing 
      multiple tubes, I demonstrate that my innovative 20-color approach enables a more 
      in-depth exploration of HSPC subpopulations and the detection of MRD with at 
      least comparable sensitivity. Furthermore, leveraging advanced analytical tools 
      such as t-SNE and FlowSOM learning algorithms, I conduct extensive cross-sample 
      comparisons with two-dimensional gating approaches. My results underscore the 
      efficacy of these two methods as powerful unsupervised alternatives for manual 
      HSPC subpopulation analysis. I expect that in the future, complex 
      multi-dimensional flow cytometric data analyses, such as those employed in this 
      study, will be increasingly used in hematologic diagnostics.
FAU - Matthes, Thomas
AU  - Matthes T
AUID- ORCID: 0000-0002-4875-2477
AD  - Hematology Service, Oncology Department, University Hospital Geneva, Rue 
      Gabrielle Perret-Gentil, 1205 Geneva, Switzerland.
AD  - Clinical Pathology Service, Diagnostics Department, University Hospital Geneva, 
      Rue Gabrielle Perret-Gentil, 1205 Geneva, Switzerland.
LA  - eng
GR  - unrestricted funding for reagents/Cytek Biosciences/
PT  - Journal Article
DEP - 20240229
PL  - Switzerland
TA  - Int J Mol Sci
JT  - International journal of molecular sciences
JID - 101092791
SB  - IM
MH  - Humans
MH  - Flow Cytometry/methods
MH  - Unsupervised Machine Learning
MH  - *Leukemia, Myeloid, Acute/drug therapy
MH  - Hematopoietic Stem Cells/pathology
MH  - *Hematopoietic Stem Cell Transplantation/methods
MH  - Neoplasm, Residual/diagnosis
PMC - PMC10932439
OTO - NOTNLM
OT  - acute myeloid leukemia
OT  - flow cytometry
OT  - hematopoiesis
OT  - leukemia
OT  - measurable residual disease
OT  - myelodysplastic syndrome
OT  - stem cells
OT  - unsupervised analysis
COIS- The author declares no conflicts of interest.
EDAT- 2024/03/13 06:46
MHDA- 2024/03/14 06:46
PMCR- 2024/02/29
CRDT- 2024/03/13 01:23
PHST- 2024/02/03 00:00 [received]
PHST- 2024/02/22 00:00 [revised]
PHST- 2024/02/26 00:00 [accepted]
PHST- 2024/03/14 06:46 [medline]
PHST- 2024/03/13 06:46 [pubmed]
PHST- 2024/03/13 01:23 [entrez]
PHST- 2024/02/29 00:00 [pmc-release]
AID - ijms25052847 [pii]
AID - ijms-25-02847 [pii]
AID - 10.3390/ijms25052847 [doi]
PST - epublish
SO  - Int J Mol Sci. 2024 Feb 29;25(5):2847. doi: 10.3390/ijms25052847.