LYN kinase programs stromal fibroblasts to facilitate leukemic survival via regulation of c-JUN and THBS1.

Microenvironmental bystander cells are essential for the progression of chronic lymphocytic leukemia (CLL). We have discovered previously that LYN kinase promotes the formation of a microenvironmental niche for CLL. Here we provide mechanistic evidence that LYN regulates the polarization of stromal fibroblasts to support leukemic progression. LYN is overexpressed in fibroblasts of lymph nodes of CLL patients. LYN-deficient stromal cells reduce CLL growth in vivo. LYN-deficient fibroblasts show markedly reduced leukemia feeding capacity in vitro. Multi-omics profiling reveals that LYN regulates the polarization of fibroblasts towards an inflammatory cancer-associated phenotype through modulation of cytokine secretion and extracellular matrix composition. Mechanistically, LYN deletion reduces inflammatory signaling including reduction of c-JUN expression, which in turn augments the expression of Thrombospondin-1, which binds to CD47 thereby impairing CLL viability. Together, our findings suggest that LYN is essential for rewiring fibroblasts towards a leukemia-supportive phenotype.

PySpacell: A Python Package for Spatial Analysis of Cell Images.

Technologies such as microscopy, sequential hybridization, and mass spectrometry enable quantitative single-cell phenotypic and molecular measurements in situ. Deciphering spatial phenotypic and molecular effects on the single-cell level is one of the grand challenges and a key to understanding the effects of cell-cell interactions and microenvironment. However, spatial information is usually overlooked by downstream data analyses, which usually consider single-cell read-out values as independent measurements for further averaging or clustering, thus disregarding spatial locations. With this work, we attempt to fill this gap. We developed a toolbox that allows one to test for the presence of a spatial effect in microscopy images of adherent cells and estimate the spatial scale of this effect. The proposed Python module can be used for any light microscopy images of cells as well as other types of single-cell data such as in situ transcriptomics or metabolomics. The input format of our package matches standard output formats from image analysis tools such as CellProfiler, Fiji, or Icy and thus makes our toolbox easy and straightforward to use, yet offering a powerful statistical approach for a wide range of applications. © 2019 International Society for Advancement of Cytometry.

Compound Functional Prediction Using Multiple Unrelated Morphological Profiling Assays.

Phenotypic cell-based assays have proven to be efficient at discovering first-in-class therapeutic drugs mainly because they allow for scanning a wide spectrum of possible targets at once. However, despite compelling methodological advances, posterior identification of a compound's mechanism of action (MOA) has remained difficult and highly refractory to automated analyses. Methods such as the cell painting assay and multiplexing fluorescent dyes to reveal broadly relevant cellular components were recently suggested for MOA prediction. We demonstrated that adding fluorescent dyes to a single assay has limited impact on MOA prediction accuracy, as monitoring only the nuclei stain could reach compelling levels of accuracy. This observation suggested that multiplexed measurements are highly correlated and nuclei stain could possibly reflect the general state of the cell. We then hypothesized that combining unrelated and possibly simple cell-based assays could bring a solution that would be biologically and technically more relevant to predict a drug target than using a single assay multiplexing dyes. We show that such a combination of past screen data could rationally be reused in screening facilities to train an ensemble classifier to predict drug targets and prioritize a possibly large list of unknown compound hits at once.

FHL2 Regulates Natural Killer Cell Development and Activation during Streptococcus pneumoniae Infection.

Recent in silico studies suggested that the transcription cofactor LIM-only protein FHL2 is a major transcriptional regulator of mouse natural killer (NK) cells. However, the expression and role of FHL2 in NK cell biology are unknown. Here, we confirm that FHL2 is expressed in both mouse and human NK cells. Using FHL2-/- mice, we found that FHL2 controls NK cell development in the bone marrow and maturation in peripheral organs. To evaluate the importance of FHL2 in NK cell activation, FHL2-/- mice were infected with Streptococcus pneumoniae. FHL2-/- mice are highly susceptible to this infection. The activation of lung NK cells is altered in FHL2-/- mice, leading to decreased IFNγ production and a loss of control of bacterial burden. Collectively, our data reveal that FHL2 is a new transcription cofactor implicated in NK cell development and activation during pulmonary bacterial infection.

In vitro and in vivo evidence for an inflammatory role of the calcium channel TRPV4 in lung epithelium: Potential involvement in cystic fibrosis.

Cystic fibrosis (CF) is an inherited disease associated with chronic severe lung inflammation, leading to premature death. To develop innovative anti-inflammatory treatments, we need to characterize new cellular and molecular components contributing to the mechanisms of lung inflammation. Here, we focused on the potential role of "transient receptor potential vanilloid-4" (TRPV4), a nonselective calcium channel. We used both in vitro and in vivo approaches to demonstrate that TRPV4 expressed in airway epithelial cells triggers the secretion of major proinflammatory mediators such as chemokines and biologically active lipids, as well as a neutrophil recruitment in lung tissues. We characterized the contribution of cytosolic phospholipase A2, MAPKs, and NF-κB in TRPV4-dependent signaling. We also showed that 5,6-, 8,9-, 11,12-, and 14,15-epoxyeicosatrienoic acids, i.e., four natural lipid-based TRPV4 agonists, are present in expectorations of CF patients. Also, TRPV4-induced calcium mobilization and inflammatory responses were enhanced in cystic fibrosis transmembrane conductance regulator-deficient cellular and animal models, suggesting that TRPV4 is a promising target for the development of new anti-inflammatory treatments for diseases such as CF.

Detection and tracking of overlapping cell nuclei for large scale mitosis analyses.

BACKGROUND: Cell culture on printed micropatterns slides combined with automated fluorescent microscopy allows for extraction of tens of thousands of videos of small isolated growing cell clusters. The analysis of such large dataset in space and time is of great interest to the community in order to identify factors involved in cell growth, cell division or tissue formation by testing multiples conditions. However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. Consequently, image analysis of those large dynamic datasets with no possible human intervention has proven impossible using state of the art automated cell detection methods. RESULTS: Here, we propose a fully automated image analysis approach to estimate the number, the location and the shape of each cell nucleus, in clusters at high throughput. The method is based on a robust fit of Gaussian mixture models with two and three components on each frame followed by an analysis over time of the fitting residual and two other relevant features. We use it to identify with high precision the very first frame containing three cells. This allows in our case to measure a cell division angle on each video and to construct division angle distributions for each tested condition. We demonstrate the accuracy of our method by validating it against manual annotation on about 4000 videos of cell clusters. CONCLUSIONS: The proposed approach enables the high throughput analysis of video sequences of isolated cell clusters obtained using micropatterns. It relies only on two parameters that can be set robustly as they reduce to the average cell size and intensity.

Alpha-tocopherol-mediated long-lasting protection against oxidative damage involves an attenuation of calcium entry through TRP-like channels in cultured hippocampal neurons.

We have reported that a transient treatment of hippocampal neurons with alpha-tocopherol induced a long-lasting protection against oxidative damage mediated by Fe(2+) ions. This protection required protein synthesis. Here, we have studied whether this "hyposensitivity" to oxidative stress could be linked to an altered Ca(2+) homeostasis. Fe(2+) ions triggered a Ca(2+) entry which was required for Fe(2+) ion-induced toxicity. This influx was sensitive to blockers of TRP-like nonspecific Ca(2+) channels, including Ruthenium Red, La(3+), and Gd(3+) ions which also prevented the Fe(2+) ion-induced toxicity and oxidative stress as revealed by protein carbonylation status. The pretreatment with alpha-tocopherol resulted in a reduction of the Ca(2+) increase induced by Fe(2+) ions and masked the blocking effect of La(3+) ions. Moreover, such a pretreatment reduced the capacitive Ca(2+) entries (CCE) observed after metabotropic glutamate receptor stimulation, which are known to involve TRP-like channels. By contrast, in a model of "hypersensitivity" to oxidative stress obtained by chronic stimulation of glucocorticoid receptors, we observed an exacerbation of the various effects of Fe(2+) ions, i.e., cellular toxicity and Ca(2+) increase, and the glutamate-stimulated CCE. Therefore, we conclude that the long-lasting neuroprotection induced by alpha-tocopherol pretreatment likely results from an attenuation of Ca(2+) entries via TRP-like channels.