ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2022
Identification of fetal liver stroma in spectral cytometry using the parameter autofluorescence.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 20 citations in OpenAlex.
- Spatiotemporal dynamics of fetal liver hematopoietic niches.The Journal of experimental medicine · 2025Article
- Spectral Algal Fingerprinting and Long Sequencing in Synthetic Algal-Microbial Communities.Cells · 2024Article
- Beyond 40 fluorescent probes for deep phenotyping of blood mononuclear cells, using spectral technology.Frontiers in immunology · 2024Article
- Panel Design and Optimization for Full Spectrum Flow Cytometry.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Impact of rising seawater temperature on a phagocytic cell population duringFrontiers in immunology · 2023Article
- Identification of fetal liver stroma in spectral cytometry using the parameter autofluorescence.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The fetal liver (FL) is the main hematopoietic organ during embryonic development. The FL is also the unique anatomical site where hematopoietic stem cells expand before colonizing the bone marrow, where they ensure life-long blood cell production and become mostly resting. The identification of the different cell types that comprise the hematopoietic stroma in the FL is essential to understand the signals required for the expansion and differentiation of the hematopoietic stem cells. We used a panel of monoclonal antibodies to identify FL stromal cells in a 5-laser equipped spectral flow cytometry (FCM) analyzer. The "Autofluorescence Finder" of SONY ID7000 software identified two distinct autofluorescence emission spectra. Using autofluorescence as a fluorescence parameter we could assign the two autofluorescent signals to three distinct cell types and identified surface markers that characterize these populations. We found that one autofluorescent population corresponds to hepatoblast-like cells and cholangiocytes whereas the other expresses mesenchymal transcripts and was identified as stellate cells. Importantly, after birth, autofluorescence becomes the unique identifying property of hepatoblast-like cells because mature cholangiocytes are no longer autofluorescent. These results show that autofluorescence used as a parameter in spectral FCM is a useful tool to identify new cell subsets that are difficult to analyze in conventional FCM.
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