ABSTRACT
Autofluorescence is an intrinsic feature of cells, caused by the natural emission of light by photo-excitatory molecular content, which can complicate analysis of flow cytometry data. Different cell types have different autofluorescence spectra and, even within one cell type, heterogeneity of autofluorescence spectra can be present, for example, as a consequence of activation status or metabolic changes. By using full spectrum flow cytometry, the emission spectrum of a fluorochrome is captured by a set of photo detectors across a range of wavelengths, creating an unique signature for that fluorochrome. This signature is then used to identify, or unmix, that fluorochrome's unique spectrum from a multicolor sample containing different fluorescent molecules. Importantly, this means that this technology can also be used to identify intrinsic autofluorescence signal of an unstained sample, which can be used for unmixing purposes and to separate the autofluorescence signal from the fluorophore signals. However, this only works if the sample has a singular, relatively homogeneous and bright autofluorescence spectrum. To analyze samples with heterogeneous autofluorescence spectral profiles, we setup an unbiased workflow to more quickly identify differing autofluorescence spectra present in a sample to include as "autofluorescence signatures" during the unmixing of the full stained samples. First, clusters of cells with similar autofluorescence spectra are identified by unbiased dimensional reduction and clustering of unstained cells. Then, unique autofluorescence clusters are determined and are used to improve the unmixing accuracy of the full stained sample. Independent of the intensity of the autofluorescence and immunophenotyping of cell subsets, this unbiased method allows for the identification of most of the distinct autofluorescence spectra present in a sample, leading to less confounding autofluorescence spillover and spread into extrinsic phenotyping markers. Furthermore, this method is equally useful for spectral analysis of different biological samples, including tissue cell suspensions, peripheral blood mononuclear cells, and in vitro cultures of (primary) cells.
ABSTRACT
BACKGROUND: Human lymph node (HuLN) models have emerged with invaluable potential for immunological research and therapeutic application given their fundamental role in human health and disease. While fibroblastic reticular cells (FRCs) are instrumental to HuLN functioning, their inclusion and recognition of importance for organotypic in vitro lymphoid models remain limited. METHODS: Here, we established an in vitro three-dimensional (3D) model in a collagen-fibrin hydrogel with primary FRCs and a dendritic cell (DC) cell line (MUTZ-3 DC). To study and characterise the cellular interactions seen in this 3D FRC-DC organotypic model compared to the native HuLN; flow cytometry, immunohistochemistry, immunofluorescence and cytokine/chemokine analysis were performed. RESULTS: FRCs were pivotal for survival, proliferation and localisation of MUTZ-3 DCs. Additionally, we found that CD1a expression was absent on MUTZ-3 DCs that developed in the presence of FRCs during cytokine-induced MUTZ-3 DC differentiation, which was also seen with primary monocyte-derived DCs (moDCs). This phenotype resembled HuLN-resident DCs, which we detected in primary HuLNs, and these CD1a- MUTZ-3 DCs induced T cell proliferation within a mixed leukocyte reaction (MLR), indicating a functional DC status. FRCs expressed podoplanin (PDPN), CD90 (Thy-1), CD146 (MCAM) and Gremlin-1, thereby resembling the DC supporting stromal cell subset identified in HuLNs. CONCLUSION: This 3D FRC-DC organotypic model highlights the influence and importance of FRCs for DC functioning in a more realistic HuLN microenvironment. As such, this work provides a starting point for the development of an in vitro HuLN.