HLA-G gene editing in tumor cell lines as a novel alternative in cancer immunotherapy.

Cancer immunotherapies based mainly on the blockade of immune-checkpoint (IC) molecules by anti-IC antibodies offer new alternatives for treatment in oncological diseases. However, a considerable proportion of patients remain unresponsive to them. Hence, the development of novel clinical immunotherapeutic approaches and/or targets are crucial.W In this context, targeting the immune-checkpoint HLA-G/ILT2/ILT4 has caused great interest since it is abnormally expressed in several malignancies generating a tolerogenic microenvironment. Here, we used CRISPR/Cas9 gene editing to block the HLA-G expression in two tumor cell lines expressing HLA-G, including a renal cell carcinoma (RCC7) and a choriocarcinoma (JEG-3). Different sgRNA/Cas9 plasmids targeting HLA-G exon 1 and 2 were transfected in both cell lines. Downregulation of HLA-G was reached to different degrees, including complete silencing. Most importantly, HLA-G - cells triggered a higher in vitro response of immune cells with respect to HLA-G + wild type cells. Altogether, we demonstrated for the first time the HLA-G downregulation through gene editing. We propose this approach as a first step to develop novel clinical immunotherapeutic approaches in cancer.

celldeath: A tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition.

Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell death is preceded by slight morphological changes in cell shape and texture. In this paper, we trained a neural network to classify cells undergoing cell death. We found that the network was able to highly predict cell death after one hour of exposure to camptothecin. Moreover, this prediction largely outperforms human ability. Finally, we provide a simple python tool that can broadly be used to detect cell death.

PIWI-interacting RNAs are differentially expressed during cardiac differentiation of human pluripotent stem cells.

PIWI-interacting RNAs (piRNAs) are a class of non-coding RNAs initially thought to be restricted exclusively to germline cells. In recent years, accumulating evidence has demonstrated that piRNAs are actually expressed in pluripotent, neural, cardiac and even cancer cells. However, controversy remains around the existence and function of somatic piRNAs. Using small RNA-seq samples from H9 pluripotent cells differentiated to mesoderm progenitors and cardiomyocytes we identified the expression of 447 piRNA transcripts, of which 241 were detected in pluripotency, 218 in mesoderm and 171 in cardiac cells. The majority of them originated from the sense strand of protein coding and lncRNAs genes in all stages of differentiation, though no evidences of amplification loop (ping-pong) were found. Genes hosting piRNA transcripts in cardiac samples were related to critical biological processes in the heart, like contraction and cardiac muscle development. Our results indicate that these piRNAs might have a role in fine-tuning the expression of genes involved in differentiation of pluripotent cells to cardiomyocytes.

Integrin alpha-5 subunit is critical for the early stages of human pluripotent stem cell cardiac differentiation.

The stem cell niche has a strong influence in the differentiation potential of human pluripotent stem cells with integrins playing a major role in communicating cells with the extracellular environment. However, it is not well understood how interactions between integrins and the extracellular matrix are involved in cardiac stem cell differentiation. To evaluate this, we performed a profile of integrins expression in two stages of cardiac differentiation: mesodermal progenitors and cardiomyocytes. We found an active regulation of the expression of different integrins during cardiac differentiation. In particular, integrin α5 subunit showed an increased expression in mesodermal progenitors, and a significant downregulation in cardiomyocytes. To analyze the effect of α5 subunit, we modified its expression by using a CRISPRi technique. After its downregulation, a significant impairment in the process of epithelial-to-mesenchymal transition was seen. Early mesoderm development was significantly affected due to a downregulation of key genes such as T Brachyury and TBX6. Furthermore, we observed that repression of integrin α5 during early stages led to a reduction in cardiomyocyte differentiation and impaired contractility. In summary, our results showed the link between changes in cell identity with the regulation of integrin α5 expression through the alteration of early stages of mesoderm commitment.

Deep Learning Neural Networks Highly Predict Very Early Onset of Pluripotent Stem Cell Differentiation.

Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recognition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train a CNN with transmitted light microscopy images to distinguish pluripotent stem cells from early differentiating cells. We induced differentiation of mouse embryonic stem cells to epiblast-like cells and took images at several time points from the initial stimulus. We found that the networks can be trained to recognize undifferentiated cells from differentiating cells with an accuracy higher than 99%. Successful prediction started just 20 min after the onset of differentiation. Furthermore, CNNs displayed great performance in several similar pluripotent stem cell (PSC) settings, including mesoderm differentiation in human induced PSCs. Accurate cellular morphology recognition in a simple microscopic set up may have a significant impact on how cell assays are performed in the near future.