A Scalable System for Production of Functional Pancreatic Progenitors from Human Embryonic Stem Cells

Development of a human embryonic stem cell (hESC)-based therapy for type 1 diabetes will require the translation of proof-of-principle concepts into a scalable, controlled, and regulated cell manufacturing process. We have previously demonstrated that hESC can be directed to differentiate into pancreatic progenitors that mature into functional glucose-responsive, insulin-secreting cells in vivo. In this study we describe hESC expansion and banking methods and a suspension-based differentiation system, which together underpin an integrated scalable manufacturing process for producing pancreatic progenitors. This system has been optimized for the CyT49 cell line. Accordingly, qualified large-scale single-cell master and working cGMP cell banks of CyT49 have been generated to provide a virtually unlimited starting resource for manufacturing. Upon thaw from these banks, we expanded CyT49 for two weeks in an adherent culture format that achieves 50–100 fold expansion per week. Undifferentiated CyT49 were then aggregated into clusters in dynamic rotational suspension culture, followed by differentiation en masse for two weeks with a four-stage protocol. Numerous scaled differentiation runs generated reproducible and defined population compositions highly enriched for pancreatic cell lineages, as shown by examining mRNA expression at each stage of differentiation and flow cytometry of the final population. Islet-like tissue containing glucose-responsive, insulin-secreting cells was generated upon implantation into mice. By four- to five-months post-engraftment, mature neo-pancreatic tissue was sufficient to protect against streptozotocin (STZ)-induced hyperglycemia. In summary, we have developed a tractable manufacturing process for the generation of functional pancreatic progenitors from hESC on a scale amenable to clinical entry

Fluoride remediation using floating macrophytes

Six aquatic macrophytes, such as Pistia stratiotes, Ceratophyllum demersum, Nymphoides indica, Lemna major, Azolla pinnata,and Eichhornia crassipes were considered for remove fluoride from aqueous solution. Five different concentrations (10, 30, 50, and 100 ppm) of fluoride solution were taken in 1 L plastic container. Fixed weight (20 g) of macrophytes along with 500 ml fluoride solution was taken in each plastic container for 72 hours observation. Results demonstrated all the macrophytes show highest fluoride removal during 24 h to 48 h, but after 72 h their efficiency reduced drastically. The species N. indica showed better removal efficiency than other experimental macrophytes. In general, pigment measurement data indicated higher concentration at 72 h. However, Pistia sp. showed higher concentration of pigmentation at intermediate time interval (48 h). Higher level of dry weight to fresh weight ratio was recorded for L. major and A. pinnata at all concentrations, excepting at 10 ppm. In addition, all macrophytes showed lower RGR at higher concentration. Isotherm study indicated that macrophyte C. demersum is a good fitted with Freundlich and Langmuir isotherm whereas L. major with Langmuir isotherm during 24 hours

Analysis of the whole transcriptome from gingivo-buccal squamous cell carcinoma reveals deregulated immune landscape and suggests targets for immunotherapy

<div><p>Background</p><p>Gingivo-buccal squamous cell carcinoma (GBSCC) is one of the most common oral cavity cancers in India with less than 50% patients surviving past 5 years. Here, we report a whole transcriptome profile on a batch of GBSCC tumours with diverse tobacco usage habits. The study provides an entire landscape of altered expression with an emphasis on searching for targets with therapeutic potential.</p><p>Methods</p><p>Whole transcriptomes of 12 GBSCC tumours and adjacent normal tissues were sequenced and analysed to explore differential expression of genes. Expression changes were further compared with those in TCGA head and neck cohort (n = 263) data base and validated in an independent set of 10GBSCC samples.</p><p>Results</p><p>Differentially expressed genes (n = 2176) were used to cluster the patients based on their tobacco habits, resulting in 3 subgroups. Immune response was observed to be significantly aberrant, along with cell adhesion and lipid metabolism processes. Different modes of immune evasion were seen across 12 tumours with up-regulation or consistent expression of <i>CD47</i>, unlike other immune evasion genes such as <i>PDL1</i>, <i>FUT4</i>, <i>CTLA4</i> and <i>BTLA</i> which were downregulated in a few samples. Variation in infiltrating immune cell signatures across tumours also indicates heterogeneity in immune evasion strategies. A few actionable genes such as <i>ITGA4</i>, <i>TGFB1</i> and <i>PTGS1/COX1</i> were over expressed in most samples.</p><p>Conclusion</p><p>This study found expression deregulation of key immune evasion genes, such as <i>CD47</i> and <i>PDL1</i>, and reasserts their potential as effective immunotherapeutic targets for GBSCC, which requires further clinical studies. Present findings reiterate the idea of using transcriptome profiling to guide precision therapeutic strategies.</p></div

Hierarchical clustering of GBSCC tumours by 2176 deregulated gene expressions.

<p>Hierarchical clusters were constructed based on log2 transformed expression values of 1002 upregulated (represented by colours of negative values in heatmap) and 1174 downregulated genes (represented by colours of positive values in heatmap). Across all 12 tumours there is a gross similarity in deregulation pattern, with some exceptions. As a result, 3 distinct sample clusters were noticed. The coloured panel below, represent subject's smoking (orange) and/or chewing tobacco (red) and/or alcohol abuse (green)status.</p

Immune response alterations in tumor compared to normal tissues.

<p>(A) Heatmap shows diverse expression levels (log fold change of FPKM values) of immune evasion genes. Orange values in color bar shows up regulation while values in blue show down regulation. The panel below shows proliferation scores per samples with green color intensity indicating higher proliferation score and numbers indicates % CCP score. The right-side panel indicates fold change values (FPKM) in TCGA HNSCC cohort (n = 263) for each gene. In case of TCGA HNCC tissues, Green color denotes downregulation while red color shows upregulation in the right-side panel. (B) The barplot shows how relative composition of immune cells is altered across 12 pairs of tumor compared to its control tissues. Plot was derived from the <i>CIBERSORT</i> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183606#pone.0183606.ref009" target="_blank">9</a>] estimated data output from FPKM normalized expression data. Every color stands for a type of immune cell and height of each colored bar represents relative frequency of an immune cell type. In the plot, 2N and 2D indicate normal and tumour tissues of tumour-normal paired S2sample, respectively. Similar nomenclature was used for tumour and normal tissues of other samples.</p

Schematic view of fusion events.

<p>(A) ANO1-PLA2G16 fusion gene which retains exon 16 along with upstream exons of ANO1 and exon 3 along with downstream exons of PLA2G16 in tumour tissue (i.e. 23D) of the tumour-normal paired S23 sample. (B) S100A9-KRT17 fusion gene deduced from coding regions up to exon 3 of S100A9 and exon 1 to all other downstream exons of KRT17 in tumour tissue (i.e. 2D) of the tumour-normal paired S2 sample.</p

Expression deregulation of miRNA and target mRNAs.

<p>(A) Bar plots show log₂fold change in expression of miRNAs (hsa-miR-18b, hsa-miR-1293and hsa-miR-21) and their target, TIMP3. Scatter plots show negative correlation of TIMP3 expression with hsa-miR-18b and hsa-miR-21expression. Negative correlation was not observed between TIMP3 and hsa-miR-1293. Negative values of log₂fold change indicate upregulated expression whereas positive log₂fold change values indicate downregulation. (B) Bar plot showslog₂fold change of expression of miRNAs (hsa-miR-126 and hsa-miR-7) and their target, IRS1. Scatter plot shows negative correlation of IRS1 expression with hsa-miR-126 and hsa-miR-7expression. Negative values of log₂fold change indicate upregulated expression whereas positive fold change values indicate downregulation.</p