Metastasis is the major cause of treatment failure and cancer-related deaths in prostate cancer (PCa) patients. Our previous study demonstrated that a CD44+ subpopulation isolated from PCa cells or tumours possesses both stem cell properties and metastatic potential, serving as metastatic prostate cancer stem cells (mPCSCs) in PCa metastasis. However, the underlying mechanisms remain unknown.
In this study, we established PCa models via the orthotopic and subcutaneous implantation of different human PCa cancer cell lines, and compared the metastatic efficacy, after which process function analysis of target genes was pinpointed.
Several novel differentially expressed genes (DEGs) between orthotopic and ectopic tumours were identified. Among them, human homeobox B9 (HOXB9) transcription factor was found to be essential for PCa metastasis, as evidenced by the diminished number of lung metastatic foci derived from orthotopic implantation with HOXB9-deficient CWR22 cells, compared with the control. In addition, HOXB9 protein expression was upregulated in PCa tissues, compared with paracancer and benign prostate hyperplasia tissues. It was also positively correlated with Gleason scores. Gain- and loss-of-function assays showed that HOXB9 altered the expression of various tumour metastasis- and cancer stem cell (CSC) growth-related genes in a transforming growth factor beta (TGFβ)-dependent manner. Moreover, HOXB9 was overexpressed in an ALDH+CD44+CXCR4+CD24+ subpopulation of PCa cells that exhibited enhanced TGFβ-dependent tumorigenic and metastatic abilities, compared with other isogenic PCa cells. This suggests that HOXB9 may contribute to PCa tumorigenesis and metastasis via TGFβ signalling. Of note, ALDH+CD44+CXCR4+CD24+-PCa cells exhibited resistance to castration and antiandrogen therapy and were present in human PCa tissues.
Taken together, our study identified HOXB9 as a critical regulator of metastatic mPCSC behaviour. This occurs through altering the expression of a panel of CSC growth- and invasion/metastasis-related genes via TGFβ signalling. Thus, targeting HOXB9 is a potential novel therapeutic PCa treatment strategy.
© 2021. The Author(s).

Imaging flow cytometry (IFC) produces up to 12 spectrally distinct, information-rich images of single cells at a throughput of 5,000 cells per second. Yet often, cell populations are still studied using manual gating, a technique that has several drawbacks, hence it would be advantageous to replace manual gating with an automated process. Ideally, this automated process would be based on stain-free measurements, as the currently used staining techniques are expensive and potentially confounding. These stain-free measurements originate from the brightfield and darkfield image channels, which capture transmitted and scattered light, respectively. To realize this automated, stain-free approach, advanced machine learning (ML) methods are required. Previous works have successfully tested this approach on cell cycle phase classification with both a classical ML approach based on manually engineered features, and a deep learning (DL) approach. In this work, we compare both approaches extensively on the problem of white blood cell classification. Four human whole blood samples were assayed on an ImageStream-X MK II imaging flow cytometer. Two samples were stained for the identification of eight white blood cell types, while two other sample sets were stained for the identification of resting and active eosinophils. For both data sets, four ML classifiers were evaluated on stain-free imagery with stratified 5-fold cross-validation. On the white blood cell data set, the best obtained results were 0.778 and 0.703 balanced accuracy for classical ML and DL, respectively. On the eosinophil data set, this was 0.871 and 0.856 balanced accuracy. We conclude that classifying cell types based on only stain-free images is possible with all four classifiers. Noteworthy, we also find that the DL approaches tested in this work do not outperform the approaches based on manually engineered features. © 2019 International Society for Advancement of Cytometry.
© 2019 International Society for Advancement of Cytometry.

CD32 has been shown to be preferentially expressed in latently HIV-1-infected cells in an in vitro model of quiescent CD4 T cells. Here we show that stimulation of CD4+ T cells with IL-2, IL-7, PHA, and anti-CD3/CD28 antibodies induces T-cell proliferation, co-expression of CD32 and the activation of the markers HLA-DR and CD69. HIV-1 infection increases CD32 expression. 79.2% of the CD32+/CD4+ T cells from HIV+ individuals under antiretroviral treatment were HLA-DR+. Resting CD4+ T cells infected in vitro generally results in higher integration of provirus. We observe no difference in provirus integration or replication-competent inducible latent HIV-1 in CD32+ or CD32- CD4+ T cells from HIV+ individuals. Our results demonstrate that CD32 expression is a marker of CD4+ T cell activation in HIV+ individuals and raises questions regarding the immune resting status of CD32+ cells harboring HIV-1 proviruses.