Category Archives: Embryonic Stem Cells

Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid … – Nature.com

By Embryonic Stem Cells

CNN architecture and dataset for estimating spatial gene expression patterns

Our model utilizes a CNN that takes a phase-contrast image as input and estimates a fluorescent image as output (Fig.1A). The typical input to a CNN is a two-dimensional (2D) image. This 2D image is passed through several convolution layers, each followed by a nonlinear activation function. The training parameters correspond to the weights of these convolution kernels and the biases. Our network has a U-Net-like architecture27, which is an encoder-decoder structure with skip connections. The embedded features from the encoder are passed through the decoder, which consists of upsampling and convolution layers to increase the resolution of the intermediate feature maps to obtain a fluorescent image as output.

In our model, the ResNet5028 was used as the backbone of the encoder. The size of the input image for the ResNet50 is (3times Htimes W). To use the pre-trained model of the ResNet50, gray-scale phase-contrast images were replicated in the axis of the channel to create three-channel images. At the first layer, a convolution with stride 2 is applied to the input image to generate features of size (64times frac{H}{2}times frac{W}{2}). The ResNet50 has 4 residual blocks and the size of the output features of these blocks are (256times frac{H}{4}times frac{W}{4}), (512times frac{H}{8}times frac{W}{8}), (1024times frac{H}{16}times frac{W}{16}), and (2048times frac{H}{32}times frac{W}{32}), respectively. These features are then concatenated to the decoder to exploit multi-scale information. The output of the decoder is a fluorescent image of size (1times frac{H}{2}times frac{W}{2}). Note that each convolution layer has a batch normalization (BN) layer and a rectified linear unit (ReLU) activation function, except for the final convolution layer, which has a sigmoid activation function to constrain the range of the output values between 0 and 1.

The network was optimized by minimizing the training loss computed on the output and corresponding ground-truth fluorescent images. The combination of mean squared error (MSE) and cosine similarity, which captures structural patterns from the entire image, was used as the training loss.

To train, validate, and test our model, we cultured retinal organoids derived from mouse ESCs using the SFEBq method10. In this culture, a GFP gene was knocked-in under the promoter of a master gene of retinal differentiation, Rax. Using this method, we obtained a dataset of a pair of phase-contrast image and fluorescent image of Rax during retinal differentiation (Fig.1B). Images were captured for 96 organoids at 4.5, 5, 6, 7, and 8days after the start of SFEBq, where each sample was captured as 14 Z-stack images. This resulted in a total of (96times 5times 14=6720) image pairs were obtained. These image pairs were divided into 5880, 420, and 420 samples for training, validation, and test, respectively. 84, 6, and 6 organoids were used for training, validation, and test, respectively; thus, each organoid does not appear in the different datasets. For data augmentation, we randomly flipped the input images vertically and horizontally during training. While the image resolution of both phase-contrast and fluorescent images is (960times 720), the (512times 512) regions where organoids appear were extracted.

To demonstrate our model, we applied it to 420 samples of the test data. As a result, the proposed model successfully estimated the spatial expression patterns of Rax from phase-contrast images during retinal organoid development (Fig.2). During development, multiple optic vesicles are formed through large and complicated deformations (Fig.2A). This process begins with a spherical embryonic body, with some portions of the tissue surface evaginating outward to form hemispherical vesicles, i.e., optic vesicles. Importantly, the resulting morphology of retinal organoids, especially optic vesicles, varies widely29. This process is known to be governed by the expression of the Rax gene (Fig.2B). That is, the Rax gene is gradually expressed in several parts of the tissue surface, so-called eye field, where cells differentiate from neuroepithelium into several types of retinal cells.

Estimated spatial Rax expression patterns during retinal organoid development. (A) Phase-contrast images from day 4.5 to day 8. (B) Captured fluorescent images of Rax as ground-truths. (C) Estimated fluorescent images with our model. (D) Error maps between captured and estimated images. The error metric was a squared error. The organoids in (AD) are identical. Scale bars indicate 200m.

Our model successfully recapitulated the above features of Rax expression (Fig.2C), i.e., the Rax intensity was relatively low and homogenous at days 4.5, 5, 6, and gradually increased around the evaginated tissue regions at days 7 and 8. Remarkably, the regions of high Rax expression were accurately estimated even in organoids with various morphologies. On the other hand, as the Rax intensity increases, especially around the evaginated tissue regions, the error of the estimated image from the ground-truth image increases with time (Fig.2D).

To quantitatively evaluate the accuracy of the estimation, we statistically analyzed the estimation results at each stage. To clarify whether the model can estimate Rax intensity in both samples with high and low Rax expression, each of the ground-truth and estimated fluorescence images was divided into two categories by the coefficient of variation of the foreground pixels in a fluorescent image at day 8 (Fig.3A). The samples in each group were labeled as positive and negative, respectively. For each of these categories, the mean and coefficient of variation of the pixel values were calculated (Fig.3BE). In calculating these values, the phase-contrast images were binarized to obtain foreground and background masks, and then computed using only the foreground pixels and normalized to those of the background pixels.

Statistical analysis of fluorescence at each developmental stage for positive and negative samples. (A) Histogram of coefficient of variation for foreground pixel values of fluorescent images at day 8. (B, C) Means of pixel values in positive and negative samples at each stage for ground-truth (green bars) and estimated fluorescent images (blue bars), respectively. (D, E) Coefficients of variation in positive samples at each stage for both ground-truth (green bars) and estimated fluorescent images (red bars), respectively. (F, G) Plots of ground-truth and estimated pixel values in positive and negative samples at each stage, respectively. Errors are 0% and 25% on the solid and dotted black lines, respectively. Error bars in (BE) indicate standard deviations.

Positive samples showed a gradual increase in mean and intensity over the days passed (Fig.3B). The negative sample, on the other hand, showed relatively low values from the beginning and did not change significantly over the days (Fig.3C). Similarly, the coefficients of variation increased in the positive samples but not in the negative samples (Fig.3D,E). These results indicate that the model successfully estimates the feature of the spatial Rax expression patterns during retinal organoid development, i.e., positive samples gradually increase Rax expressions and their heterogeneity, but negative samples do not. The intensity of the estimated images is relatively lower than the intensity of the ground-truth images in the positive samples and vice versa in the negative samples.

To clarify whether the model is capable to estimate intermediate values of the Rax expression, we analyzed the correlations between ground-truth and estimated values on foreground pixels at each stage, respectively (Fig.3F,G). The results show that in the positive sample (Fig.3F), the distribution of intensities is initially concentrated at low intensities and gradually expands to high intensities as the day progresses, with a wide distribution from low to high intensities. Similarly, in the negative sample, the luminance distribution is initially concentrated at low intensities, but does not expand as much as in the positive sample (Fig.3G). These results indicate that the model successfully estimated the plausible values across all pixel intensities, demonstrating the capability of our method to infer intermediate levels of gene expression. Notably, predicting Rax expression in the organoids at later stages, such as day 8 in our experiments, becomes more feasible for the model due to their characteristic morphologies. These distinct morphologies provide features that can be efficiently extracted by the convolution operators of the model.

To determine whether the estimated Rax expression patterns correspond to tissue morphologies, we quantified the spatial distribution of Rax intensity and the mean curvature along the tissue contour around each optic vesicle (Fig.4). For this analysis, four typical optic vesicles were selected from the positive samples and their curvature and Rax distribution were quantified. In this analysis, tissue contours were extracted and the radius of a circle passing through three points on the tissue contour was calculated as the inverse of the curvature. Moreover, the Rax intensity was measured as the average value along the depth direction from the tissue contour.

Correlation analysis of spatial Rax expression patterns and optic-vesicle morphologies. (A) Phase-contrast images. (B) Captured fluorescent images of Rax as ground-truths. (C) Estimated fluorescent images with our model. (D) Mean curvatures as a function of the distance along the organoid contour. (E) Captured and estimated fluorescent intensities of Rax along the organoid contour. The organoids in (AC) are identical and captured on day 8. The mean curvatures and fluorescence in (D, E) are for the regions indicated by the red line starting from the red dot in (A). Scale bars indicate 200m.

Optic vesicles are hemispherical, with positive curvature at the distal portion and negative curvature at the root (Fig.4A,D). The Rax intensity is continuously distributed around each vesicle, being highest at the distal part and gradually decreasing toward the root (Fig.4B,E). Furthermore, because the test images were taken with a conventional fluorescence microscope, structures above and below the focal plane are included in each image. Therefore, although some images have multiple overlapping vesicles (e.g., samples iii and iv), the model successfully estimated the Rax intensity of the overlapping regions as well.

MSE is commonly used as the training loss for training regression models. In addition to MSE, this model also uses cosine similarity, which can capture structural patterns from the entire image. To analyze the effect of cosine similarity on the estimation accuracy, we tested the model with different weights for both error metrics (Fig.5). The trained models were evaluated with MSE for each test dataset on different days (Fig.5A). The results demonstrated that cosine similarity improved the estimation accuracy at the early and intermediate stages, such as from day 4.5 to day 6. At these stages, the intensity in the differentiated region is weak, making it difficult for the network to capture structural patterns using MSE alone. Cosine similarity, on the other hand, enabled the network to learn the patterns from the weak intensity by calculating the correlation between the normalized ground-truth and the estimated images (Fig.5B). Therefore, our model has the capability to achieve the best estimate at different stages with appropriate weight balancing.

Effects of the balance of training loss on estimation accuracy. (A) Mean squared error at each stage with different hyperparameters, where bold and underlined numbers stand for the best and second best results on each day, respectively. (B) Examples of estimated fluorescent images at days 6 and 8 with different hyperparameters. The MSE of each estimated image is described in the upper left. The results with the lowest MSEs are surrounded by the red boxes. Scale bars indicate 200m.

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Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid ... - Nature.com

Scientists Created a Monkey With Two Different Sets of DNA – Smithsonian Magazine

By Embryonic Stem Cells

The monkey "chimera" with two sets of DNA at three days old. Some body parts appear tinted green, because the researchers marked the transplanted cells with fluorescent dye to trace what parts they developed into. Cell / Cao et al.

Researchers have created a monkey with two different sets of DNAby injecting stem cells from one monkey embryo into another of the same species. This method has been used in rats and mice beforebut the recent feat marks the first time ever that it has been successful in another animal, including primates. Scientists say the breakthrough could help with medical research in the future.

This is a long-sought goal in the field, Zhen Liu of the Chinese Academy of Sciences (CAS) says in a statement. This work could help us to generate more precise monkey models for studying neurological diseases as well as for other biomedicine studies.

However, the monkey had to be euthanized after ten days due to breathing issues and hypothermia, which some scientists say highlights ethical concerns in this type of research, reports Nature News Carissa Wong.

In reference to the mythological, fire-breathing chimera that has a lions head, a goats body and a serpents tail, individuals that contain two or more different sets of DNA in their bodies are referred to as chimeric by scientists. Chimerism can occur naturally, as when one embryo in a set of fraternal twins dies in the womb and the other absorbs its cells. This has been documented in several species of birds, reptiles and mammals, including humans.

But chimerism can also occur artificially with an organ or bone marrow transplant. In this case, the researchers transplanted stem cells, which can develop into any kind of cell.

To create the monkey chimera, Liu and his colleagues removed stem cells from seven-day-old embryos of long-tailed macaques (Macaca fascicularis). They labeled these with green fluorescent protein so that any tissue the cells created in a chimeric monkey could be visually identified later. They then injected these cells into four- to five-day-old embryos of the same species and implanted them into 40 female macaques.

Of these surrogate mother monkeys, 12 became pregnant, and 6 gave birth to live young. The teams analysis showed that just one live-birth male and one miscarried male were substantially chimeric. In the live monkey, donor cells made up 67 percent of its tissues on average, but across the 26 different tissue types tested, that number ranged between 21 percent and 92 percent.

Scientists saw evidence of glowing green fluorescencethe mark of the donor cellsin the live monkeys fingertips and around its eyes. Percentages of donor cells in the miscarried fetus were lower. The team published its research this month in the journal Cell.

It is a very good and important paper, Jacob Hanna, a stem cell biologist and embryologist at the Weizmann Institute of Science in Israel who was not involved with the study, tells CNNs Katie Hunt. This study may contribute to easier and better making of mutant monkeys, just like biologists have been doing for years with mice. Of course, work with [nonhuman primates] is slower and much harder but is important.

Researchers have been creating chimeric mice since the 1960s to learn more about critical developmental processes, including how stem cells grow into more specialized cells. Theyve also used the mice as models to study diseases. But trying to understand humans by looking at rodents has its limitations.

Mice dont reproduce many aspects of human disease for their physiology being too different from ours, Liu tells CNN. In contrast, human and monkey are close evolutionary, so human diseases can be more faithfully modeled in monkeys.

In controversial research, scientists have previously created human-monkey chimeric embryos, though these only grew for 20 days before being destroyednot long enough to develop a brain or nervous system. Some scientists hope these techniques could be used to grow human organs inside other animals for transplantation, per Nature News. But such efforts involving animalsespecially once human cells are addedcan quickly pose ethical quandaries.

All animal research warrants careful consideration, but this is particularly important for all non-human primate research, stem cell researcher Megan Munsie, of the University of Melbourne and Murdoch Childrens Research Institute, tells Peter de Kruijff of the Australian Broadcasting Corporation (ABC).

Munsie notes to the publication that, of all 74 chimeric monkey embryos transferred into surrogate mothers in the recent study, only one living macaque produced the desired resultsand it had to be euthanized. Future efforts should focus on improving embryo viability to avoid the high abortion rate and associated distress and waste, she adds.

Additionally, long-tailed macaques, while commonly used as lab monkeys, were listed as endangered by the International Union for Conservation of Nature last year. Munsie suggests limiting research to animals that are not endangered, per the ABC. The authors, however, say this research could help with conservation efforts.

Monkey chimeras also have potential enormous value for species conservation if they could be achieved between two types of nonhuman primate species, one of which is endangered, co-author Miguel Esteban, of the Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, and a researcher with BGI-Research Hangzhou, tells CNN. If there is contribution of the donor cells from the endangered species to the germ line, one could envisage that, through breeding, animals of these species could be produced.

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Scientists Created a Monkey With Two Different Sets of DNA - Smithsonian Magazine

Stem Cell Banking Market Size Revenue Hits $18.04 Billion by 2032 … – GlobeNewswire

By Embryonic Stem Cells

Newark, Nov. 20, 2023 (GLOBE NEWSWIRE) -- The Brainy Insights estimates that the USD 7.93 Billion in 2022stem cell banking market will reach USD 18.04 Billion by 2032. As stem cell transplants become more viable therapeutic options, the demand for a reliable and secure source of stem cells has increased significantly. Stem cell banks are critical to the success of these treatments because they provide a secure and dependable means of storing and transferring stem cells for transplantation.

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Report Coverage Details

Key Insight of the Stem Cell Banking Market

Asia Pacific is anticipated to expand at the highest CAGR of 10.55% over the projection period.

Asia Pacific is expected to grow at the highest CAGR of 10.55% over the forecast period. It is due to increased public knowledge of stem cell's medical potential, as well as increased government spending in stem cell research and development. For many years, India has been at the forefront of medical advancements as one of the most popular foreign destinations for medical tourism. Furthermore, the development of novel treatments and procedures, as well as the higher success rate of stem cell treatment, are likely to drive expansion in the region's stem cell banking business.

The adult stem cells segment is expected to register the highest CAGR of 10.32% over the projected period in the stem cell banking market.

The adult stem cells segment is anticipated to grow at the highest CAGR of 10.32% in the stem cell banking market. The growing understanding of the variety and effectiveness of adult stem cell banking services is driving up demand. Adult stem cell preservation is being considered by patients, physicians, and researchers as a proactive strategy to future disease problems. This need promotes business competitiveness and innovation, resulting in enhanced storage systems and broader service offers.

Over the projected period, the sample preservation and storage segment is expected to register the highest CAGR of 10.73% in the stem cell banking market.

Over the forecasted period, the sample preservation and storage segment is anticipated to grow at the highest CAGR of 10.73% in the stem cell banking market. This vital service area includes cutting-edge cryopreservation processes, cutting-edge storage facilities, and stringent quality control systems. In this age of regenerative medicine, the efficiency of stem cell treatments is dependent on the quality and accessibility of preserved samples.

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Market Dynamics

Driver: A growing elderly population

An older population has a favourable impact on the market. This demographic shift is changing healthcare dynamics all around the world. As people age, they become more susceptible to degenerative diseases such as osteoarthritis, cardiovascular disease, and neurological disorders such as Alzheimer's and Parkinson's. Stem cells have immense promise for repairing damaged or ageing tissues, paving the way for new treatments and better quality of life for the elderly. This ageing population necessitates more modern healthcare treatments and represents a significant client base for stem cell banking services. Many people and families are aware of the option of keeping stem cells from themselves or loved ones, which can be taken from sources such as cord blood or adipose tissue. These stem cells can be used in future therapies to combat age-related health issues, offering comfort and hope.

Opportunity: Growing ethical issues over the use of embryonic stem cells

The market is being fueled by growing ethical concerns about the use of embryonic stem cells. Because embryonic stem cell research involves the killing of embryos, it has long been a subject of ethical debate, leading in moral and legislative constraints in a variety of domains. This has shifted the emphasis of stem cell research and therapeutic applications away from controversial sources and towards non-controversial sources such as adult stem cells and cord blood. As a result, it is becoming popular among individuals and institutions seeking the potential benefits of stem cell therapy without the ethical ambiguity of stem cell banking. Cord blood, in particular, has grown in prominence as a rich source of stem cells that is ethically sound. Families and healthcare practitioners recognise the value of keeping these cells as a form of biological insurance against future illnesses for the donor and potentially compatible family members.

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Some of the major players operating in the stem cell banking market are:

Cordlife Cryo-Save AG (A Group of Esperite) Stemcyte Smart Cells International Ltd. Cordvida CBR Systems, Inc. Lifecell Cryoviva India Cryo-Cell Viacord

Key Segments cover in the market:

By Product Type:

Human Embryonic Cells Adult Stem Cells IPS Cells

By Service Type:

Sample Analysis Sample Collection and Transportation Sample Preservation and Storage Sample Processing

By Region

North America (U.S., Canada, Mexico) Europe (Germany, France, U.K., Italy, Spain, Rest of Europe) Asia-Pacific (China, Japan, India, Rest of APAC) South America (Brazil and the Rest of South America) The Middle East and Africa (UAE, South Africa, Rest of MEA)

About the report:

The market is analyzed based on value (USD Billion). All the segments have been analyzed worldwide, regional, and country basis. The study includes the analysis of more than 30 countries for each part. The report analyzes driving factors, opportunities, restraints, and challenges for gaining critical insight into the market. The study includes porter's five forces model, attractiveness analysis, product analysis, supply, and demand analysis, competitor position grid analysis, distribution, and marketing channels analysis.

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The Brainy Insights is a market research company, aimed at providing actionable insights through data analytics to companies to improve their business acumen. We have a robust forecasting and estimation model to meet the clients' objectives of high-quality output within a short span of time. We provide both customized (clients' specific) and syndicate reports. Our repository of syndicate reports is diverse across all the categories and sub-categories across domains. Our customized solutions are tailored to meet the clients' requirements whether they are looking to expand or planning to launch a new product in the global market.

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Stem Cell Banking Market Size Revenue Hits $18.04 Billion by 2032 ... - GlobeNewswire

Metastases and treatment-resistant lineages in patient-derived … – Nature.com

By Embryonic Stem Cells

Selection of patients and 2DOs

Eight patients with distant metastases or recurrences that could be evaluated using computed tomography were selected. All patients underwent baseline imaging within 4 weeks before anticancer drug administration. The tumor volume and reduction rate were measured according to RECIST guidelines42. 2DOs were established from primary CRC specimens and cultured according to a previous report20 and stocked at our laboratory cell bank. Briefly, CRC tissue from resected specimens was minced into 1-mm pieces and dissociated with 1mg/mL of collagenase (C6885; Sigma-Aldrich, St. Louis, MO, USA). Filtered cell pellets between 20m and 200m were seeded in plates coated with iMatrix-511 (Takara Bio Inc., Kusatsu, Japan) and cultured in medium containing 10ng/mL of basic fibroblast growth factor (ThermoFisher Scientific, Waltham, MA, USA) and 2ng/mL of transforming growth factor beta (R&D Systems Inc., Minneapolis, MN, USA) to maintain heterogeneous primary culture cells. Sixteen 2DOs with stable culture and drug sensitivity on testing, including eight 2DOs from patients with distant metastases or recurrences, were selected for further analysis.

The human colorectal tumor cell lines, HCT116, gifted by Dr. Bert Vongelstein (Johns Hopkins University, Baltimore, MD, USA), and HT29 (EC91072201, ECACC), were cultured in Dulbeccos modified Eagles medium supplemented with 10% fetal bovine serum (ThermoFisher Scientific), 1% GlutaMAXI (ThermoFisher Scientific), and 1% penicillin/streptomycin/amphotericin B (Wako Pure Chemical Industries, Osaka, Japan). Cells were incubated at 37C in a humidified atmosphere containing 5% CO2. Cells were harvested using 0.25% Trypsin-EDTA (ThermoFisher Scientific) for further analysis.

Cellartis human iPS cell line 12 (ChiPSC12) cells (Takara Bio) were cultured in the Cellartis DEF-CS 500 Culture System (Takara Bio). Cells were incubated at 37C in a humidified atmosphere containing 5% CO2. Cells were harvested using Accutase (Innovative Cell Technologies, Inc., San Diego, CA) for further analysis.

The expression of proteins in cells was determined using flow cytometry. Cultured cells were dissociated with Accutase (Nacalai Tesque Inc., Kyoto, Japan). CTCs were isolated from clinical blood samples using OncoQuick (Greiner BioOne, Frickenhausen, Germany) according to the manufacturers protocol. Cells were stained with antibodies targeting EpCAM, CD133, CD44, CD41, CD45, and LGR5 (Supplementary TableS2). For detecting POU5F1, a True-Nuclear Transcription Factor Buffer Set (424401; BioLegend) was used. After staining cell surface proteins, cells were fixed and stained with antibodies for POU5F1, according to the manufacturers protocol. Relative fluorescent intensities were measured with an SH800 cell sorter (SONY, Tokyo, Japan) and cell morphology and staining locations were also measured with a FlowSight imaging flow cytometer (Merck-Millipore, Darmstadt, Germany). 7-AAD (Miltenyi Biotec, San Diego, CA, USA) was used to analyze living cells. A dimensionality reduction step in two dimensions was performed using t-distributed stochastic neighbor embedding (t-SNE) to visualize high-dimensional data of stem cell marker expression. Data were analyzed using FlowJo 10.2 software (FlowJo LLC, Ashland, OR, USA).

Anticancer drug sensitivity was examined in sixteen 2DOs within 510 passages. Drugs and their concentrations in clinical drug assays are listed in Supplementary TableS3. The number of viable cells in each well was measured using a Cell Counting Kit-8 (Dojindo Laboratories, Kumamoto, Japan) before drug administration and 96h after drug administration. Cell proliferation in DMSO and distilled water, which were used to dilute each drug, were used as controls. The ratio of the number of living cells after administering the drug to the control is shown. Three independent experiments were performed and the average is shown. The formula used for calculation was as follows: 100Cont. 0h cell num.Drug 96h cell num./{(Cont. 96h cell num.Cont. 0h cell num.) Drug 0h cell num.}

Regarding the sensitivity of each anticancer drug, a dimensionality reduction step in two dimensions was performed using t-SNE to visualize high-dimensional data for 21 drugs in a low-dimensional space. The statistical analyses were performed using R 3.6.3 (R Core Team, 2018), with the data.table (v1.12.8; Dowle & Srinivasan43), t-SNE (Krijthe44), and ggplot2 (Wickham45) packages.

Total RNA was extracted using an RNA Purification Kit (Qiagen, Hilden, Germany). TruSeq Stranded mRNA Library Prep (Illumina, San Diego, CA, USA) was used to prepare RNA-seq libraries from the total RNA (1g). Multiplexed libraries were sequenced on an Illumina NextSeq with single-end 75-bp sequencing. RNA-seq data were mapped to the hg38 genome release using the bioinformatic pipeline of the Illumina Base Space Sequence Hub and the Subio software platform (Subio, Inc., Kagoshima, Japan).

The vector, PL-SIN-Oct4-EGFP, kindly provided by James Ellis (Addgene plasmid #21319; http://n2t.net/addgene:21319)22, was used to establish cells expressing EGFP under the OCT4 (POU5F1) promoter. The vector was transfected into 2DOs and cell lines using Lentiviral High Titer Packaging Mix with pLVSIN (Takara Bio). EGFP-positive cells were purified by sorting using a SH800 cell sorter (SONY) at least twice. POU5F1 expression was confirmed by polymerase chain reaction (PCR).

Total RNA was isolated using an RNA Purification Kit (Qiagen). Quantitative assessment was performed by real-time PCR using 100nM universal probe libraries, 0.1 FASTStart TaqMan Probe Master (Roche Diagnostics, Basel, Switzerland) for designed primers, iTaq Universal SYBR Green Supermix (Bio-Rad, Hercules, CA, USA) for commercially available primers, 100nM primers, and 10ng cDNA for cDNA amplification of target genes. Primers are listed in Supplementary TableS4. PCR was performed with 20L of the master mix in each well of a 96-well plate, and signals were detected with the CFX Connect Real-Time PCR Detection System (Bio-Rad). The thermocycler was programmed for one cycle at 95C for 10min, followed by 40 cycles at 94C for 10s, 60 C for 20s, and 72 C for 1s. cDNAs from NTERA-2 cells were used as positive controls.

A subcutaneous model was established to investigate the ability to differentiate from a single sorted cell. A single sorted cell was cultured in a dish for expansion using the 2DO culture methods described above. Accutane-dissociated cells (1106 cells) suspended in Matrigel (BD Biosciences, Franklin Lakes, NJ, USA) were subcutaneously transplanted into the dorsal flanks of 7-week-old, non-obese diabetic/severe combined immunodeficient mice (CLEA, Tokyo, Japan). The average weight was 27g at the start of the experiments. The mice were sacrificed when the tumors reached a diameter of 10mm. For the liver metastasis model, live cells (1106 cells) were sorted by 7-AAD (Miltenyi Biotec) according to EGFP expression using a SH800 cell sorter (SONY) and injected into the spleen. Liver metastasis was assessed every 4 weeks. Mice were sacrificed 8 weeks after injection for the assessment of liver metastases in the POU5F1 expression metastatic ability experiment and 10 weeks after injection in the XAV939 experiment.

Xenograft tumors were fixed in formalin, processed through a series of graded concentrations of ethanol, embedded in paraffin, and sectioned. Sections were stained with hematoxylin and eosin (H&E). Three-dimensional (3D)-formed 2DOs cultured on a NanoCulture plate were collected and centrifuged at 400g for 5min at room temperature. The pellet was consolidated using iPGell (GenoStaff Co., Ltd., Tokyo, Japan) and fixed in formalin. The pellet was processed through a series of graded concentrations of ethanol, embedded in paraffin, sectioned, and stained with H&E.

Xenograft tumors were also fixed in 10% buffered formalin and embedded in paraffin blocks. For cultured 2DOs, 3D-formed 2DOs cultured on an Ultra-Low Attachment Multiple Well Plate (Corning, NY, USA) were collected and centrifuged at 400g for 5min at room temperature. They were embedded in paraffin blocks using iPGell (GenoStaff). A 3-m section was obtained from each block. Sections were deparaffinized, and slides were boiled for 15min. Expressions of CD44, CK20, MUC2, and chromogranin A were quantified using antibodies (Supplementary TableS5). The slides were incubated with a primary antibody for 60min at room temperature and then incubated with a secondary antibody for 30min at room temperature. Slides were mounted in Prolong Gold with DAPI (Invitrogen, Waltham, MA, USA). Mucus production ability was assessed via Alcian blue staining (pH 2.5).

Cultured cells were fixed with 4% formaldehyde and blocked. They were incubated with primary antibodies (Supplementary TableS6) overnight at 4C. Cells were incubated with secondary antibodies for 90min. Slides were mounted in Prolong Gold with DAPI (ThermoFisher Scientific) overnight.

The vector, pLV[Exp]-Neo-CMV>DsRed_Express2, was constructed by VectorBuilder, Inc. (Chicago, IL, USA) (Supplementary Fig.S27). This vector was transfected into 2DOs and iPS cells using Lentiviral High Titer Packaging Mix with pLVSIN (Takara Bio). DsRed_Express2-positive cells were selected by antibiotic selection using G418 (10131035; ThermoFisher Scientific) and sorted twice by an SH800 cell sorter (SONY). All cells expressing DsRed-Express2 were detected by an SH800 cell sorter (SONY).

The vector, PL-SIN-Oct4-EGFP, kindly provided by James Ellis (Addgene plasmid #21319)22, and the vector, pMSCV-F-del Casp9.IRES.GFP, kindly provided by David Spencer (Addgene plasmid # 15567)46, were used to establish cells expressing EGFP under the OCT4 (POU5F1) promoter with inducible caspase 9. Sequence-encoding caspase 9 was digested with restriction enzymes, XhoI (R0146S; New England Biolabs, Beverly, MA, USA) and EcoRI-HF (R3101S; New England Biolabs). The DNA fragment of caspase 9 was extracted from E-Gel CloneWel 0.8% (G6500ST; ThermoFisher Scientific) using the E-Gel Power Snap Electrophoresis System (ThermoFisher Scientific) (Supplementary Fig.S28). The fragment was amplified using CloneAmp HiFi PCR Premix (Z9298N; Takara Bio) with designed primers (FW_gaattctgcagtcgatcgagggagtgcaggtgg, RV_ccgcggtaccgtcgacttagtcgagtgcgtagtc). The vector, PL-SIN-Oct4-EGFP, was linearized by a restriction enzyme, SalI-HF (R3138S; New England Biolabs). The amplified fragments and linearized vector were used for the cloning reaction by the In-Fusion HD Cloning Kit (Z9648N; Takara Bio). The transformation procedure was performed using Competent High E. Coli DH5 (TYB-DNA903; Toyobo, Osaka, Japan), and the plasmid was extracted using the Qiagen Plasmid Plus Midi Kit (12945; Qiagen). The nucleotide sequence of the vector was confirmed by Sanger sequencing, performed by GENEWIZ Japan Corp. (Kawaguchi, Japan). Primer extension sequencing was performed using Applied Biosystems BigDye version 3.1, and the reactions were then run on an Applied Biosystem 3730xl DNA Analyzer. The constructed vector was transfected into two 2DOs (603iCC and 25DiCC) using Lentiviral High Titer Packaging Mix with pLVSIN (Takara Bio). EGFP-positive cells were cloned by single-cell sorting using an SH800 cell sorter (SONY). POU5F1 expression was confirmed by PCR, and a decrease in the number of EGFP-positive cells was confirmed by the administration of B/B Homodimerizer (Z5059N; Takara Bio). The mean provirus copy number was 6.05 (1.16, n=6), as measured using the Let-X Provirus Quantitation Kit (Z1239N; Takara Bio).

603iCC-transfected POU5F1-EGFP cells with inducible caspase 9 (4.5104/well) were seeded, and 5M B/B Homodimerizer (Takara Bio) was administered for three days. Four days after the dimerizer was removed, live cells were sorted using an SH800 cell sorter (SONY) as day 7 cells. For cells not treated with a dimerizer, live cells were also sorted as day 0 cells. Single-cell library preparation was performed following the manufacturers instructions for the Chromium Next GEM Single Cell 3 Reagent Kit (v3.1) (10x Genomics, Pleasanton, CA, USA), and the libraries were sequenced on a HiSeq X sequencer (Illumina). To generate a data matrix, the Cell Ranger pipeline (v4.0.0) was applied, and raw reads were aligned to the human reference genome (GRCh 38) using the STAR aligner. For GFP transcript mapping, the GFP sequence (XM_013393261) was added to the reference fastq and gtf files. Data were deposited in Gene Expression Omnibus under the accession number GSE169220.

Seurat (version 3.2.0)47 was used for quality control and downstream analysis. Poor-quality cells were filtered out using the following parameters: nFeature_RNA 2009000 and percent.mt <10. A total of 6942 cells (control: 3342 cells and day 7: 3602 cells), which passed the quality control, were finally used for further analysis. Mitochondrial genes were filtered by mt.percent (<10). UMAP visualization was used for dimensionality reduction analysis with the following parameters: resolution, 0.5; and perplexity, 20. Marker genes discriminating the different clusters were identified using the FindAllMarkers function (min.pct=0.25 and log[fold-change] >0.25). Pathway enrichment analysis was performed using Enrichr48 (https://maayanlab.cloud/Enrichr/). To construct a single-cell pseudotime trajectory, the Monocle3 (v0.2.2) algorithm was applied (https://cole-trapnell-lab.github.io/monocle3/). After converting the Seurat object using the as.cell_data_set function, the root node was assigned to cluster 4, and the orderCells function was used to assign cells a pseudotime value. To subdivide cells based on their branch in the trajectory, the choose_graph_segments function was applied, and cluster 6 was chosen as an ending node.

Western blot analysis was performed to examine proteins associated with the Wnt/-catenin signaling pathway. Cells were lysed in 50mM TrisHCl (pH 7.6), 1% Nonidet P-40, 150mM sodium chloride, and 0.1mM zinc acetate in the presence of protease inhibitors. Protein concentration was determined by the Lowry method (Bio-Rad), and 20g of each sample was separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis. The gel was transferred electrophoretically onto a polyvinylidene difluoride membrane (Millipore, Billerica, MA, USA). The membrane was blocked with blocking buffer for 1h and then incubated overnight at 4 C with primary antibodies against -catenin (1:1000, 8480, Cell Signaling Technology), Wnt-3a (1:5000, GTX128101, Gene Tex, CA, USA), and HistoneH3 (1:2000, 4499S, Cell Signaling Technology). After a 2-h incubation with the secondary antibody, horseradish peroxidase-conjugated rabbit antibody (1:400, 7074S, Santa Cruz Biotechnology Inc., Dallas, TX, USA), protein bands were visualized using an ECL detection kit (ThermoFisher Scientific) according to the manufacturers instructions.

DNA samples were treated with sodium bisulfite using a bisulfite conversion kit (Zymo Research EZ DNA methylation Kit). After treatment, unmethylated cytosines convert to uracil, while methylated cytosines remain unchanged. Bisulfite-converted DNA samples were analyzed using the Infinium MethylationEPIC BeadChip Kit (Illumina). Bisulfite-converted DNA samples were denatured and neutralized by alkali. The denatured samples were then amplified by whole-genome amplification (37C overnight). Amplified DNA samples were enzymatically fragmented for 1h at 37C in a microsample incubator. 2-Propanol was added to the fragmented DNA samples and precipitated by centrifugation. Precipitated DNA samples were resuspended with hybridization buffer and incubated for 1h at 48C in a hybridization oven. Fragmented and resuspended DNA samples were denatured for 20min at 95C in a microsample incubator. Denatured DNA samples were dispensed onto BeadChips using a TECAN System. The BeadChips were incubated overnight at 48 C in the hybridization oven to hybridize the samples onto the BeadChips. After hybridization, seals were removed from the hybridized BeadChips. Next, unhybridized fragment DNAs were washed away. Labeled nucleotides were added to the washed BeadChips to extend primers which hybridized to the DNA. BeadChips were stained, then coated for protection, and dried. Dried BeadChips were scanned with the iSCAN System. Illumina GenomeStudio software (V2011.1) loaded the signal intensity files of BeadChips, and beta values were decided via normalization and background subtraction. Next, a comparative analysis was executed based on the Illumina Custom Model algorithm, and difference scores for all probes were computed. The markers with signal intensities adequate to distinguish between the signal and background noise were used in subsequent analysis. The markers with high scores (highly methylated and highly unmethylated compared to the reference sample) were extracted, and clustering analysis was conducted.

The NANOG binding consensus sequence is generally known to be 5TAAT[GT][GT]3 or 5[CG][GA][CG]C[GC]ATTAN[GC]3. Therefore, in the sequence of focus, the CGCCCAGTGTC part is quite similar to the binding sequence. We used Protein Data Bank data, including 4RBO, to predict binding conformations to the NANOG protein with the wild-type sequences or methylated sequence with our original method49. A sufficient amount of water molecules was placed around the complex structures, and thermodynamical sampling was performed under a periodic boundary condition. After stabilizing the complex structure by energy minimization calculations, some molecular dynamics simulations were performed at ~37C (310K) to capture the molecular behavior under the biological environment. After a sufficient thermal equilibration process, the molecular vibrations of the bonding configurations were sampled. All these calculations were performed using the AMBER package. The distributions of the interaction energy between DNA and NANOG protein were calculated by extracting 2000 conformations of complex structures from the trajectory with the abovementioned molecular dynamics simulations. Each binding energy was calculated using Gaussian program packages50 with the AMBER99 Force field level51.

603iCC cells (1104 per well) were seeded into 96-well plates and incubated for 48h. After incubation, cells were exposed to different concentrations of XAV939 (BD248591; BLD Phamatech Ltd., Shanghao, China) for 96h. The percentage of viable cells was determined using a cell counting kit solution (CCK-8; Dojindo Molecular Technologies) according to the manufacturers protocol.

Prior to cancer cell seeding, plates were coated. iPS cell-coated plates were seeded into 12-well plates (2105 iPS cells/well) 2 days prior to seeding. iPS cells were tagged with DsRed-Express by the aforementioned methods. Laminin coatings were prepared using iMatrix-511 (T304, Takara Bio) according to the manufacturers protocol. Sorted POU5F1-positive cells (2105/ well) were seeded on these plates. Medium was prepared with XAV939 (10M) for the XAV939 group and DMSO (0.3%) for the control group. All medium exchanges were performed every other day, and cells in the collected supernatant were analyzed by an SH800 cell sorter (SONY). Cells not expressing DsRED-Express2 were counted as cancer cells.

Stained specimens were analyzed using ImageJ software52. Five independent images were collected for each sample and the areas of protein expression in the samples were measured. The value was normalized by dividing by the number of cells stained with DAPI.

As an evaluation of XAV939, sorted POU5F1-positive cells were directly injected into the spleen of mice (1106 cells). After recovering from anesthesia, mice were randomly allocated to the control (0.3% DMSO that is the final concentration of DMSO in XAV939 group) or XAV939 group (100g/injection/mouse). XAV939 (CS-0494, ChemScene, Monmouth Junction, NJ, USA) was administered by intraperitoneal injection at 1mg/mL (injection volume, 100L) every day for 8 weeks, followed by 2 weeks of observation. Ten weeks after injection, mice were sacrificed for the assessment of metastases. Mouse body weight was measured twice per week, and no weight gain or loss greater than 5% was observed.

The Osaka University Review Board, the OICI Review Board, approved this study, and written informed consent for the study was obtained from all participants according to the ethics guidelines. All ethical regulations relevant to human research participants were followed. The OICI Animal Research Committee approved this study, and we have complied with all relevant ethical regulations for animal use. All experimental protocols were in accordance with the guidelines of the Osaka University, the OICI, and Declaration of Helsinki.

Continuous variables are expressed as the mean with standard error of the mean. The significance of the difference between the two groups was analyzed using the x2 test and Wilcoxons signed rank-sum test. All data were analyzed using JMP software (SAS Institute), R 3.6.3, and Prism 8 (GraphPad Software, San Diego, CA, USA). Results were considered statistically significant at P<0.05.

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On this day: Mahmoud Abbas becomes leader of Fatah in 2004 – In-Cyprus

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Following are some of the major events to have occurred on November 25:

1935 King George II returns to Greece as monarch.

1936 Germany and Japan sign anti-Comintern pact.

1952 Agatha Christies play The Mousetrap opened in London. Still playing to audiences today, it holds the record for the longest continuous run of any show in the world.

1963 U.S. President John F. Kennedy was buried with full military honours at Arlington National Cemetery, three days after his assassination.

1974 The Burmese diplomat U Thant died. He became U.N. secretary-general after the death of Dag Hammarskjold in 1961, and held the post until 1971.

1997 Malawis former leader, Kamuzu Banda, died aged 99. As Hastings Banda, he became president in 1966 and proclaimed himself ruler for life in 1971. He was defeated in 1994 in Malawis first democratic election.

1999 Six-year-old Cuban Elian Gonzalez survives smuggling boat shipwreck on its way to the United States, sparking a controversial custody case between the two countries.

2001 Advanced Cell Technology Inc. of Massachusetts became the first organisation to report the successful cloning of a human embryo. The company said it did not intend to create a human being but to use the stem cells to treat disease.

2004 The dominant Palestinian political faction, Fatah, approved Mahmoud Abbas as its candidate to succeed Yasser Arafat, who had died on Nov. 11.

2005 Richard Burns, the only Englishman to win the world rally championship, died of a brain tumour at the age of 34.

2015 Pope Francis arrives in Kenya on historic African visit.

(Reuters)

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Origin and distribution of macrophages

Macrophages are ubiquitous in any part of the body and perform three essential functions, namely phagocytosis, exogenous antigen presentation, and secretion of cytokines and growth factors for immunomodulation. They perform important duties in tissue development, homeostasis, clearance of dead cells and foreign pathogens, and modulation of inflammatory and tumoral immune responses [32,33,34]. Macrophages also have different names and functions in different tissues, such as circulating monocyte-derived macrophages, tissue-resident macrophages (TRMs), and tumor-associated macrophages, which have complex correlations in terms of classification and origin. TRMs perform appropriate functions in various tissues of the body, including microglia in the brain, Kupffer cells in the liver, and Langerhans cells in the skin [35, 36], and it is currently believed that most of the population of TRMs originates from embryonic precursors in the yolk sac and fetal liver and that they self-maintain independently of the myeloid cells in adulthood [37, 38]. TAMs, on the other hand, consist mainly of circulating monocyte-derived macrophages and RTMs recruited by tumors into TME and are one of the important targets for tumor immunotherapy [39].

Macrophages are significant plastic and their activation state is influenced by a multitude of factors, but they can usually be simplified into two classifications based on stimulatory factors and secretory products (Fig.2), namely classically activated M1 macrophages and alternatively activated M2 macrophages [40]. Although this M1/M2 dichotomization simplifies the differences in phenotypic and functional continuum changes in macrophages, this terminology is still more commonly used when discussing whether macrophages are more biased toward a pro-inflammatory or anti-inflammatory phenotype [41].

Macrophage activation and M1/M2 typing. Macrophages polarized into classically activated (M1) or alternatively activated (M2) macrophages under the influence of different cytokines or other factors secrete different cytokines to change the cellular microenvironment to a pro-inflammatory or anti-inflammatory state, exerting anti-tumor or pro-tumor effects at the tumor site

M1 macrophage polarization is usually driven by granulocytemacrophage colony-stimulating factor (GM-CSF), lipopolysaccharide (LPS), IFN-, TNF-, and PAMPs [42]. M1 phenotype macrophages have mainly pro-inflammatory properties, promoting the pro-inflammatory response of helper T cells 1 (Th1) by secreting cytokines, such as TNF-, IL-1, IL-12, and IL-18, and enhancing the recruitment of Th1 cells to sites of inflammation by secreting chemokines, such as chemokines CXC motif ligand 9 (CXCL9) and CXCL10 [43]. M1 macrophages can trigger an adaptive immune response through self-mediated cytotoxicity or cross-presentation of antigens (TAAs and TANs), triggering potent anti-tumor immunity. Therefore, M1 macrophages are considered a tumor-suppressive macrophage phenotype [44].

M2 macrophage polarization is usually driven by macrophage colony-stimulating factor (M-CSF), IL-4, IL-10, IL-13, and transforming growth factor- (TGF-) [45]. M2 macrophages have a critical position in appropriate immune function and homeostasis in vivo, with examples including stimulation of Th2 cell responses, mediation of parasite clearance, immunomodulation, wound healing and tissue repair [46]. However, the function of M2 macrophages can also be adversely affected by tumor exploitation by producing immunosuppressive and pro-angiogenic factors such as IL-10, arginase 1 (ARG1), TGF-, or vascular endothelial growth factors (VEGFs), which stimulate tumor cell proliferation, invasion, metastasis, and angiogenesis [41]. Therefore, M2 macrophages are considered a tumor-supporting macrophage phenotype[47].

TAMs are a collective term for macrophages that are prevalent in tumors and can account for up to 50% of some solid tumors [48]. TAMs also share the markers of M1/M2 macrophages [49], however, TAMs rarely exhibit a true M1 or M2 phenotype and are more aptly referred to as M1-like/M2-like TAMs [50]. Under the effects of tumor-secreted colony-stimulating factor 1 (CSF-1, or M-CSF), TAMs polarize to M2-like, allowing immunosuppressive M2-like TAMs to predominate in tumors [47, 51]. High infiltration of M2-like TAMs reduces therapeutic efficacy, shaping tumor-supportive TME, angiogenesis, fibrosis, immunosuppressive cell recruitment, lymphocyte rejection, drug resistance, invasion, and metastasis to enhance tumor progression [52,53,54], which are often associated with poor clinical outcomes [55,56,57].

TAMs are effective target cells in immunotherapy of tumors [12, 58]. This is because macrophages exert opposite anti-tumor or pro-tumor functions through a range of activation pathways and/or different macrophage populations [13, 59]. Different approaches can be taken to eliminate tumor-promoting macrophages and activate or transform them into tumor-suppressing macrophages. Common therapeutic strategies are inhibition of TAMs recruitment [60, 61], reprogramming of TAMs to an M1-like phenotype [62,63,64], and depletion of TAMs [65, 66].

Macrophage plasticity influences tumor progression and treatment outcome and has a similar effect in oncolytic virotherapy. When OVs are delivered to the body, the body triggers innate immunity in response to the foreign invasion of viral infection. Monocytes, macrophages and NK cells will recognize and remove some of the OVs and play a certain inhibitory role. However, in this process, macrophages will also act as carriers of OVs to tumor cells. At the same time macrophages enhance polarization toward a pro-inflammatory phenotype, and this local immune response is also critical for initiating initial anti-tumor immunity [67]. Therefore, we need to further comprehend the complex interactions among OVs, macrophages, and tumors (Fig.3), to elucidate the mechanisms of macrophages that limit or promote the tumoricidal effects of OVs, and to better utilize the advantages of macrophages to enhance the anti-tumor benefits in future oncolytic virus therapeutic strategies.

Interaction of OVs, macrophages, and tumor cells. After OVs are delivered, some OVs are attacked by activated monocytes/macrophages, causing the viral titer of OVs to decrease. Another portion of OVs can be transported to the tumor site for viral replication, lysing tumor cells and releasing viral progeny, damage-associated molecular patterns (DAMPs), pathogen-associated molecular patterns (PAMPs), and tumor-associated antigens (TAAs). Antigen-presenting cells (APCs) take up and present these antigens, and the resulting activated antigen-specific CD8+T cells as well as natural killer (NK) cells exert antitumor effects. Secreted IFN- and PAMPs repolarize pro-tumorigenic M2-like macrophages into anti-tumorigenic M1-like macrophages, and the anti-tumor/viral effects of the immune system can be further enhanced by secreting IFN- and TNF-

In general, macrophages show antiviral activity in the setting of oncolytic virotherapy, which is consistent with their defense against pathogens.

Among the routes of administration of OVs, intravenous has more potential than intra-tumoral injection in the treatment of systemic metastatic tumors. However, intravenously administered OVs are often hindered by circulating and tissue immune complexes, neutralizing antibodies, and innate immune cells before reaching the tumor site. Activated macrophages have multiple viral clearance mechanisms, including virus recognition through PRRs, cytokine responses such as IFN, phagocytosis, and activation of other immune cells to reduce viral titers delivered to the tumor site [68, 69].

In a glioma model, phagocytosis by macrophages limits the spread of OVs. Delivery of oncolytic herpes simplex virus (oHSV) after the depletion of macrophages can increase viral titers at tumor sites [70]. IFN and TNF- signaling is an important mechanism for the antiviral effects of macrophages [71, 72]. In ovarian and breast cancer models it was shown that macrophages can activate the tumor cell JAK/STAT pathway and upregulate the expression of interferon-stimulated genes (ISGs), with tumor cells thereby acquiring an antiviral status that makes them resistant to OVs [73]. In a study of glioblastoma (GBM) treated with oHSV, macrophages, and microglia were found to be the main producers of TNF-, which inhibits viral replication. Brief administration of TNF- blockers effectively enhances the killing of tumor cells while reducing inflammation-induced neurotoxicity, enhancing viral replication and survival in GBM intracranial tumors [69]. TAMs and microglia in malignant gliomas largely limit the activity of OVs [74].

Although inflammatory cytokines and phagocytosis produced by macrophages are powerful weapons to kill tumor cells, they also reduce the efficiency of transport of OVs to tumors, so direct delivery of OVs requires a larger viral load to counteract this clearance effect and increases the viral titer of transport to tumor sites.

However, on the other hand, the interaction between macrophages and OVs could enhance the antitumor effect.

First of all, macrophages can act as carriers of OVs for transport. Macrophages have shown antiviral effects to some extent, but interestingly, increasing studies have evidenced that viruses can utilize monocytes/macrophages as vectors for spreading and replication [75], and macrophages may be an integral part of the therapy of OVs, possibly due to the higher susceptibility of monocytes or nave macrophages to OVs [76]. Previous research has found that monocytes/macrophages in peripheral blood can act as viral vectors, transporting viable viral particles to tumor sites. Follow-up after intravenous administration of the eutherian virus recovered replicative and oncolytic eutherian virus in blood mononuclear cells even in the presence of neutralizing antibodies (nAbs) to the virus [77]. In another study with oncolytic adenovirus, it was shown that, possibly due to the very low expression of viral antigens, macrophages can act as silent vectors that hide and support viral replication, allowing adenovirus delivery to the tumor site and produce a long-lasting therapeutic effect [78]. More interestingly, recent preclinical studies have found that macrophages are not only capable of uptake and delivery of the tumor oncolytic virus HSV1716 but also support HSV1716 replication within macrophages, which could enhance the effect of viral therapy [79].

Second, OVs can enhance the phagocytic activity of macrophages on tumor cells. As mentioned earlier, TAMs are an important component of macrophages. Activation of TAMs to produce phagocytic activity is a novel mechanism of tumor killing [80], which can be activated by oncolytic virus treatment. CD47 is a membrane-bound protein that is highly expressed on tumor cells and binds to signal regulatory protein (SIRP) on macrophages, delivering a don't eat me signal that leads to immune evasion by the tumor [81]. After OVs infect cells, PAMPs are exposed to the host immune system, inducing endoplasmic reticulum stress and ICD, leading to the release of DAMPs [82,83,84], which include calreticulin (CRT). CRT, an endoplasmic reticulum-associated molecular chaperone, can also block the CD47 receptor on tumor cells, thereby reducing the don't eat me signals generated by macrophages and DCs in response to CD47 binding, and attenuating immune evasion by tumor cells [85]. In addition, after OVs interacted with the B cell receptor (BCR), activated B cells were able to release neutralizing antibodies that mediated NK cell antibody-dependent cytotoxicity (ADCC) and macrophage antibody-dependent cell phagocytosis (ADCP) of virus-infected tumor cells, activating phagocytosis of tumor cells by innate immune cells [86].

Most importantly, OVs can induce polarization of TAMs towards an anti-tumor phenotype. OVs induce activation of NK cells and macrophages through PRRs recognizing PAMPs and DAMPs, secretion of inflammatory cytokines such as IFN-, and induced macrophage polarization to M1-like, which results in diminished immunosuppression of TAMs [76, 87]. In an in vitro model of breast cancer, it was found that irrespective of the initial polarization state of macrophages, treatment with oncolytic measles virus (MeV) and mumps virus (MuV) resulted in a significant increase in the M1 macrophage marker, CD80, in human monocyte-derived macrophages (MDMs), while inducing anti-tumor cytokines IL-1, TNF-, CXCL9, CXCL10, and IL -6 concentrations were elevated [88]. Preclinical and clinical studies in gastric cancer or glioma have found that treatment with HSV-1 or oncolytic adenovirus rapidly recruited inflammatory cells to the injected lesions, significantly increased the intra-tumoral infiltration of M1-like macrophages and NK cells, with a reduction in the expression of M2-like macrophages, and a significant elevation of the pro-inflammatory cytokines IFN- and TNF- [89, 90]. Although oncolytic adenovirus shifts human macrophages from a more pro-tumor phenotype to a less favorable phenotype, this phenotypic shift is not complete and the M2 trait is not completely lost at the level of gene expression, immunophenotype, and cytokines, which is consistent with the concept that the M1/M2 typing of macrophages is not completely extreme, but rather sequential in phenotype and function [91].

Due to the multifaceted effects generated by macrophages in the treatment of OVs, eliminating the limiting effect of macrophages on OVs, exploiting the effectiveness of macrophages, and obtaining better therapeutic results require intensive research. The current directions are mainly the following: (1), arming OVs to enhance the beneficial effects (pro-inflammatory phenotypic polarization and phagocytosis) or attenuate the adverse effects (antiviral and pro-tumorigenic effects); (2), combining with other drugs to increase the antitumor efficacy; and (3), augmenting the targeting of OVs to tumor cells through effective carrier delivery.

OVs can be genetically engineered to arm viruses, and different immunomodulatory genes for arming OVs are being actively tested. Various OVs expressing pro-inflammatory cytokines, chemokines, and other immune checkpoint-associated molecules have been developed to enhance the anti-tumor effects of macrophages (Fig.4A).

Basic macrophage strategies in oncolytic virotherapy. Currently, there are two major directions of basic strategies for targeting the macrophage to optimize therapeutic response. On the one hand, armed OVs enhance the anti-tumor effect of macrophages. A Repolarization to an antitumor phenotype. Given the pro-tumorigenic role of M2-like tumor-associated macrophages (TAMs), the expression of pro-inflammatory cytokines or chemokines by genetically modified viruses was used to increase macrophage activity and promote the polarization of M2-like macrophages to M1-like macrophages. B Enhancement of phagocytosis by macrophages. The expression of anti-CD47 antibody or SIRP-Fc fusion protein after viral genetic modification can disrupt don't eat me signaling and enhance the killing of tumor cells by macrophages. On the other hand, weakening the clearance of OVs by macrophages contributes to higher viral titers at tumor sites. C Direct macrophage depletion. Since OVs are subject to phagocytosis by macrophages and/or clearance by antiviral cytokines after delivery, brief administration of macrophage depletion agents prior to OVs treatment can cause apoptosis of macrophages, increase the titer of OVs, and change the phenotype of TAMs. D Delivered through the carrier. In addition, the use of tumorophilic carrier cells or liposomes to deliver OVs, is also able to avoid the negative effects of neutralizing antibodies and/or innate immune cells and overcome the challenges of systemic administration of OVs

A high M2/M1 ratio in TAMs is strongly associated with tumor progression and poor prognosis. Although OVs can inherently promote polarization of M1-like TAMs and reduce the number of M2-like TAMs, armed OVs can further enhance this polarization.

Talimogene laherparepvec (T-VEC), a GM-CSF-expressing HSV-1, is the first OVs approved by the U.S. Food and Drug Administration (FDA) for the treatment of patients with advanced melanoma, with favorable safety and therapeutic outcomes [92]. This is due to the ability of GM-CSF-expressing OVs to attract monocytes and differentiate them into macrophages and DCs, repolarize TAMs from an M2-like phenotype to an M1-like phenotype, and increase the expression of the pro-inflammatory cytokines TNF-, IL-6, and IL-10 [93, 94].

IL-12 is one of the major regulators of anti-tumor immune responses, promoting the maturation of NK cells, DCs, and T cells, inducing M1-like polarization of macrophages, and increasing IFN- levels [95]. Many OVs are currently modified and produce IL-12 [96], and in a GBM model, the use of an oHSV expressing murine IL-12 (G47-mIL12) increased polarization of M1-like TAMs (iNOS+ and pSTAT1+), which may be due to IL-12-induced increases in IFN- in the TME [97].

Although IL-12 can effectively induce antitumor immunity, it has certain toxic side effects after systemic administration [95], and IL-21 may be a safer cytokine compared to IL-12. In a pancreatic cancer model study, it was demonstrated that treatment with VVL-21, an oncolytic vaccinia virus (VV) that expresses IL-21, increased the expression of M1-like macrophage marker major histocompatibility complex II (MHC II) and cytokine gene transcripts (IL-6/IL-12 and COX2), and decreased the expression of M2 macrophage marker (CD206) and cytokine gene transcripts (IL-10, TGF-, and CCL22) expression while also increasing M1 polarization in nave macrophages [98]. In addition, an IL-36-expressing VV (IL-36-OVs) was developed. It induces infiltration of lymphocytes and DCs, reduces MDSCs and M2-like TAMs, and has shown significant therapeutic effects in a variety of mouse tumor models [99].

OVs with chemokines are able to effectively recruit immune cells with antitumor effects to migrate to infected tumor sites. Chemokine CC motif ligand 5 (CCL5) promotes immune cell chemotaxis by interacting with chemokine CC motif receptor 1 (CCR1), CCR3, and CCR5 [100]. Infection of tumor cells with CCL5-expressing OVs significantly enhances the migration and activation of NK cells, macrophages, and T cells, and also activates the secretion of CXCL9 by macrophages and DCs aggregated in tumors by binding to tumor cells to activate Fc receptor-mediated ADCC in NK cells and ADCP in macrophages [101, 102], which in turn further promotes the infiltration of circulating T cells into tumor tissues [103].

Both CD40 and OX40 and their ligands CD40L and OX40L belong to the TNF receptor superfamily (TNFRSF). The interaction of CD40 and CD40L activates APCs [104], and the interaction of OX40 and OX40L activates T cells [105], which promotes antitumor effects through activated downstream signaling pathways. A CD40L-expressing oncolytic adenovirus (TMZ-CD40L) is effective in treating pancreatic cancer, a tumor with a high level of M2 macrophages, by increasing the infiltration of M1-like macrophages and T cells into the tumor, repolarizing M2-like macrophages, and controlling tumor progression [106]. Also in a pancreatic cancer model, the use of HSV-1 expressing murine OX40L ((OV-mOX40L) triggered an OX40-OX40L signaling pathway-mediated response that also reprogrammed macrophages and neutrophils to an anti-tumor state, enhanced the anti-tumor response of T cells, and significantly prolonged the survival time of mice [107].

At the same time, it is desired to modify OVs to further block the immunosuppressive effect and enhance phagocytosis of tumors by macrophages (Fig.4B). An engineered oHSV equipped with a full-length anti-CD47 antibody can be used to disrupt the don't eat me signaling generated by the CD47/SIRP pathway. This oHSV activated phagocytosis and cytotoxicity of tumor cells by macrophages and NK cells, prolonging the survival of glioblastoma and ovarian cancer model mice [108, 109]. Accordingly, investigators designed a VV capable of expressing a chimeric molecule (SIRP-Fc) consisting of the ectodomain of SIRP and the Fc structural domain of IgG4. SIRP-Fc was able to disrupt CD47/SIRP interactions by blocking CD47 in tumor cells, redirecting macrophages to the tumor site and killing the tumor cells. This VV exerted potent anti-tumor activity in a mouse model of osteosarcoma and can be broadly applied to tumors expressing CD47 [110].

Recently, in a study on cholesterol metabolism, progress has also been made in relation to macrophage phagocytic activity. This study found that TAMs in GBM accumulate cholesterol abnormally, leading to dysfunctional phagocytosis [111]. Apolipoprotein A1 (ApoA1) is a cholesterol reverse transporter protein that allows cholesterol efflux from TAMs, thereby restoring their phagocytosis and antigen-presenting role. Therefore, the investigators developed an ApoA1-expressing oncolytic adenovirus (AdVAPOA1) to intervene in cholesterol metabolism in GBM. AdVAPOA1 activated the TAM-T cell axis and downregulated immune checkpoints after intra-tumor administration, inducing systemic tumor-specific immune memory [111]. This study proposes an immunometabolic treatment approach to armed OVs.

Genetically modified OVs not only enhance anti-tumor immunity in macrophages, but also circumvent the detrimental effects of macrophages, including reducing M2-like TAMs and attenuating macrophage-restricted effects on OVs.

Currently, a panel of oncolytic adenoviruses (EnAd) expressing bivalent T-cell engagers (BiTEs) has been designed to target the immunosuppressive effects of M2-like TAMs. The BiTEs recognize CD3 on T cells and CD206 or folate receptor (FR) on M2-like macrophages. Use of such OVs in patients with malignant ascites activates T cells to selectively kill M2-like macrophages, thereby preserving M1-like macrophages and repolarizing the microenvironment toward a pro-inflammatory state [112].

Human species C adenovirus (HAdv-C5) is bound by immunoglobulin M (IgM) and coagulation factor X (FX) in the blood when delivered intravenously [113, 114], leading to the sequestration of OVs in liver-resident macrophages (Kupffer cells), limiting their tumor targeting and leading to hepatotoxicity [115]. Based on these, the investigators constructed the HAdv-C5 capsid-modified viral variant Ad5-3M. Ad5-3M is resistant to IgM- and complement-mediated inactivation, reduces internalization of the viral variant by Kupffer cells, and circumvents the adverse effects of innate immunity to OVs. In mice with disseminated lung tumors, Ad5-3M prolonged survival and improved safety and efficacy after intravenous administration of OVs [116]. Therefore, the use of genetic modification to change some protein sites in OVs to enhance their resistance is also a worthy direction.

In addition to modifying the OVs' own properties, finding the appropriate drugs for combination therapy opens up more possibilities. These strategies include combining immune checkpoint inhibitors to enhance antitumor effects, and combining macrophage depleting agents or immunosuppressive drugs to increase the titer of OVs.

Combination therapy with OVs and immune checkpoint inhibitors (ICIs) is a common combination strategy in clinical trials today (Table 1), due to the ability of OVs to increase the sensitivity of tumor cells to ICIs, which has demonstrated a strong therapeutic effect in a wide range of tumor treatments [117,118,119]. In a GBM model, the use of IL-21-expressing VV (VVDTK-STCDN1L-mIL21) in combination with systemic anti-programmed death receptor 1 (anti-PD1) therapy showed significant induction of M1-like macrophage polarization in the tumor during treatment, along with increased activation of M0 macrophages (MHC II+) in the spleen and DCs in the lymph nodes [120]. Similarly, in other GBM and triple-negative breast cancer models, combination treatment of engineered OVs with ICIs such as anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4) antibody, anti-PD-1 antibody and anti-programmed cell death ligand 1 (anti-PD-L1) significantly inhibited tumor growth. The results showed an increase in the proportion of M1-like TAMs, CD4+ and CD8+ cells, and a decrease in the number of immunosuppressive cells such as Tregs. The application of ICIs prevented immune escape from the tumor and overcame the immunosuppressive microenvironment, which is of great significance for the effective eradication of the tumor [97, 121].

OVs combined with macrophage-depleting agents have been reported to remodel TME. In macrophage-dependent tumors, investigators tested the effectiveness of clodronate liposomes and trabectedin in the oHSV treatment of Ewing's sarcoma [122]. Clodronate liposomes can transiently deplete macrophages throughout the body and have demonstrated their therapeutic potential in applications in a variety of tumors [70, 123]. Trabectedin is a chemotherapeutic agent that depletes monocytes/macrophages, including TAMs, by activating caspase-8-dependent apoptosis through the TRAIL receptor [65]. Both drugs were found to enhance antitumor efficacy after macrophage depletion (Fig.4C). Clodronate liposomes induced antitumor gene expression in TAMs, trabectedin lowered the number of intratumoral MDSCs and M2-like macrophages, and the combination of both drugs with OVs significantly changed the phenotype of TAMs and tended the immune microenvironment to an inflammatory state [122].

Inhibition of macrophage-associated pathways has also shown good efficacy in combination with other immunologic agents. The phosphatidylinositol-3-kinase (PI3K) pathway has an important part in tumor development. PI3K signaling is a key driver of macrophage M2 polarization [124, 125]. PI3K, one of the classes I PI3K isoforms, is hyper-enriched in leukocytes, of which macrophages are included [126]. Some investigators have demonstrated that treatment with PI3K inhibitors prior to intravenous delivery of VV significantly improves VV delivery to tumors and enhances tumor efficacy. This was achieved by interfering with the RhoA/ROCK, AKT, and Rac signaling pathways to inhibit viral attachment to macrophages, independent of viral internalization by macrophages [127]. They combined a PI3K inhibitor (CAL-101), engineered VV, and -PD1 for the treatment of pancreatic cancer in mice, and the results showed strong synergistic effects, demonstrated the effectiveness of systemic administration, and broke through a major limitation in the treatment of OVs [98]. In addition to this, the use of rapamycin in oncolytic virotherapy has added new possibilities. Rapamycin has immunosuppressive properties and it is able to reduce type I IFN production by inhibiting mammalian target of rapamycin complex 1 (mTORC1) [128], reduce infiltration of CD68+ microglia and CD163+ macrophages in gliomas, and increase viral replication and therapeutic efficacy within tumors [129].

Although suppression of the antiviral immune response of macrophages is beneficial in enhancing the therapeutic effect of OV, such immunosuppression may impair the functional balance of macrophages in vivo and diminish the effect of virus-mediated immune stimulation against cancer. Delivery of OVs using carrier cells with tumorophilic properties can effectively avoid the influence of the immune system and reduce the neutralization and clearance of OVs before they reach the tumor (Fig.4D). Therefore, this approach may be a more desirable strategy to improve the pharmacokinetics and biological distribution of OVs and has been extensively studied in carrier cells such as mesenchymal stem cells (MSCs), T cells, myeloid cells, and neural stem cells [130].

Moreover, the use of tumor cell tropism to enhance tumor targeting has also been studied accordingly. Membrane-encapsulated oncolytic adenovirus from cancer cells delivered intravenously was able to effectively avoid the antiviral effects of neutralizing antibodies and the innate immune system. This system increases viral replication and enhances the ability of macrophages and DCs to present tumor antigens, and has shown good efficacy in the treatment of different mouse tumor models [131]. When using VV in hosts with pre-existing antibodies to poxviruses, the transient use of a combination of multiple immunosuppressive drugs and cancer cells as carrier cells significantly improves therapeutic efficacy. Although this approach is achieved by increasing the polarization of immunosuppressive M2-like TAMs, such changes are necessary in the long run [132].

Encapsulation of OVs via liposomes (LPs) is also one of the attractive nano-delivery systems. Encapsulation of oncolytic adenovirus (Ad[I/PPT-E1A]) into liposomes coupled to chemokine CC motif ligand 2 (CCL2), which upon intravenous delivery binds to circulating monocytes expressing chemokine CC motif receptor 2 (CCR2), takes advantage of the aggregation of monocytes to hypoxic tumor vessels to deliver encapsulated OVs targeting tumor sites [133]. This system can avoid recognition and delivery to the tumor site by the immune system after intravenous delivery, reducing the number of TAMs located near the blood vessels [134].

Therefore, the use of carriers for adjuvant delivery of OVs is one of the promising strategies. This approach evades the capture of OVs by innate immune cells without affecting the body's immune function, while enhancing the targeting of tumors and reducing the viral delivery load.

In conclusion, macrophages are an important factor affecting the therapeutic effect of OVs, and in the face of this dual effect, how to seek benefits and avoid harm is something we need to consider.

Continue reading here:
Improving the therapeutic efficacy of oncolytic viruses for cancer ... - Journal of Translational Medicine

Patients accept therapy using embryonic stem cells for Parkinson’s … – BMC Medical Ethics

By Embryonic Stem Cells

Discrete choice experiment

Preferences of patients with PD for potential cell-based therapies to treat PD were assessed by a Discrete Choice Experiment (DCE) in Swedish patients with PD. The DCE is a cross-sectional survey method to investigate individuals preferences and can be used to determine the relative importance of different characteristics of an intervention and predict uptake of different interventions [15]. Respondents of a DCE are faced with a set of hypothetical choice questions with two or more alternatives, characterized by different characteristics (i.e., attributes) with varying levels. The DCE method also allows for the calculation of attribute trade-offs [16].

We performed a scoping literature review to identify attributes of treatments for PD that potentially were of importance for patients with PD when choosing treatment. Qualitative and quantitative papers investigating preferences of patients with PD related to treatment for PD were included. All literature searches were performed in PubMed and the keywords used were Parkinson disease, patient preferences, preferences, treatment, medication, and attributes. We identified 193 papers, including 29 papers that were relevant for this project, of which 20 papers remained after excluding duplicates. After reading the full text papers, 209 potential attributes were identified. Out of the 209 attributes identified in the scoping literature review, 115 attributes were unique. These attributes were condensed down to 45 by merging similar concepts. The identified attributes were discussed in a group consisting of a representative patient of a Parkinson patient organization, neurologists, a research coordinator, a nurse working with patients with PD, and researchers knowledgeable in DCE methodology. Based on the discussions in this group, 11 attributes remained. We let 17 patients with PD rank the 11 attributes from most to least important, for their decision about PD treatment. Based on the mean ranks of the attributes and discussions with clinicians, eight attributes remained. These were re-categorized into the five attributes that were assigned relevant levels to be assessed by the DCE: (i) type of treatment, (ii) aim of treatment, (iii) available knowledge of the different types of treatments, (iv) effect on symptoms, and (v) risk for severe side effects (Table1).

We followed methodological guidelines to estimate the sample size needed to identify preferences of patients with PD and differences within those preferences [17]. We considered the number of attributes in the DCE (Table1) and the number of choice questions for each respondent (n=9). Based on the sample size requirements for a DCE and accounting for subgroup analysis, we aimed for a sample size of 500 respondents.

Patients with PD were recruited from neurology clinics at two university hospitals in Sweden. This study was approved by the Swedish Ethical Review Authority (Dnr 201906539). Information about the study was sent out by mail to all potential respondents fulfilling the inclusion criteria: patients diagnosed with PD, 18 years or older, able to read and understand Swedish. Patients with a known dementia diagnosis were excluded. Information about the study was sent out to 1266 patients. Patients who had not responded within two weeks were sent a reminder by mail. All respondents provided their informed consent before entering the survey. Two patients formally declined participation, and five patients were unable to participate due to technical or health-related restrictions. In total, 498 patients participated in the study (i.e., 39% response rate).

This survey was administered as a web-based survey that included three parts: (i) information about the attributes and levels, (ii) the DCE with hypothetical choice scenarios, and (iii) demographic and attitude questions (see supplementary file for survey). The survey was created for this study and administered using Sawtooth Software (Sawtooth Software Inc.). Each respondent was faced with nine hypothetical choice scenarios that each included three alternatives. The respondents were asked to select the alternative that they most preferred out of the three presented to them. The first two alternatives were experimentally designed to assess preferences for potential treatment alternatives for PD and the third was a fixed profile (i.e., nonexperimental) to represented standard care (drugs) for patients with PD (Fig.1). We used a Bayesian D-efficient design to construct the choice scenarios for the DCE using the NGene program (version1.2.1; ChoiceMetrics 2012). Prior information on the attribute importance was gathered from a pilot test (n=142) in patients with PD. The design used 500 Halton draws and 1000 repetitions. Using the pilot data, a multinomial logit (MNL) model was fitted, and the beta estimates was used as priors for the final experimental DCE design.

Some conditions were posted on the design: if the aim of treatment was to repair damage caused by disease, the treatment could not consist of electric stimulation or drug. If the aim of the treatment was to slow down disease progression, the treatment could not consist of electric stimulation. The final discrete choice survey consisted of 36 unique choice scenarios divided into four blocks; each respondent was randomized into one block and answered nine choice scenarios. The choice questions also included a hover function with further explanations of the attributes and the levels (see Table1 for full description of the attribute levels).

Example of a choice scenario

The demographical and attitude questions included background questions (e.g., age, gender, and education) and disease-related questions (e.g., disease duration, treatment, and side effects). Moreover, the respondents attitudes were gauged with a ranking exercise with eight statements that they were asked to place in the order they found most important. The attitude questions asked respondents about their moral stands on the status of an embryo, and a ranking exercise to prioritize eight statements.

The respondents were asked about their views on how to regard the products left over after IVF procedures, which may be used for hESC isolation, that is, the blastocyst. Whether this material was regarded as a lump of cells or something more was used to dichotomize the answers. Questions to assess respondents health literacy [18] and health numeracy [19] were also included to define the sample.

The statistical analyses, in particular the estimation of the latent class model were performed using R 4.0.2 (R Core Team, 2018), the mlogit (version 1.1-1; Yves Croissant, 2009) and the gmnl (version 1.13.3; Mauricio Sarrias, 2017) [20].

Demographics describing the populations age, gender, country of birth, occupational situation, education, health numeracy, health literacy, drug frequency, disease duration, number of experienced side effects, and experience of advanced treatment were presented in mean, median, and percentages. The overall level of health literacy and numeracy was calculated for each respondent. Individuals who responded strongly disagree or disagree to one of the items were categorized as having low health literacy. Individuals who responded with neither agree nor disagree with one of the items were categorized as having medium health literacy. Individuals responding agree or strongly agree to all the items were categorized as having high health literacy, and likewise for numeracy.

Respondents attitude toward the moral status of a couple of days old human embryo was assessed using this question: The human is perceived to have a special moral position, in the sense of having rights just by being human. What moral position does a human embryo that is only a few days old have? The respondents had four statements from which to select: (1) The embryo is just a lump of cells; it is meaningless to discuss its moral status, (2) The embryo has a moral status that is in between being just a lump of cells and being a human being, (3) The embryo in its moral status is closer to being a human than just a lump of cells, and (4) The embryo has the same moral status as a human being. The variable was dichotomized based on the frequency of the data. Respondents answering The embryo is just a lump of cells; it is meaningless to discuss its moral status formed one group, and the rest another group. One-way analysis of variance and nonparametric measures were used to test the differences between the personal characteristics and the different perceptions of whether an embryo is more than a lump or cells or not.

The most important attitudinal statement was given a 1, the second most important the number 2 and so forth. The ranking exercise was illustrated with a boxplot by the median value of each statement, stratified on the different perceptions of whether an embryo is more than a lump of cells.

The latent class analysis was based on the a priori hypothesis that the authors thought would be associated with the willingness to accept a new treatment. Five variables were tested for class membership: (1) a summary of experience of different treatment, (2) experience of the summary of different side effects, (3) the perception of the moral status of the embryo, (4) experience of advanced treatment, and (5) the importance of religion. A sum of how many treatments each respondent had was calculated, and also how many side effects they had experienced. Advanced treatment was based on treatment experience with one or more of apomorphine subcutaneous injection, apomorphine subcutaneous infusion, deep brain stimulation, levodopa-carbidopa intestinal infusion, and levodopa-entacapone-carbidopa intestinal infusion. The variable the perception of the moral status of the embryo did not influence class membership and was therefore not included in the final class assignment model.

The statistical analyses of the preference data were based on a latent class model. A preference weight (i.e., coefficient) and a corresponding SE were estimated for all but one level of each attribute (i.e., reference attribute level) [21]. Dummy coding of the variables was selected for this analysis (i.e., corresponding to zero as the reference value). Each p-value is a measure of the statistical significance of the difference between the estimated preference weights for each level of the attribute compared to the reference attribute level. All results were considered statistically significant at p<0.05. Confidence intervals (95%) were also provided for each preference weight. The Akaike information criterion (AIC) and the log-likelihood values were considered when selecting the appropriate model.

The latent class model was used to identify hidden (latent) classes of respondents preferences [22]. In latent class analysis, unobserved preference heterogeneity among respondents preferences is modeled as classes with similar preference patterns but with different variances across classes. Once preference patterns have been stratified into classes, the model determines the extent to which demographic characteristics impact the likelihood of belonging to a certain class. The systematic utility component (V) describes the latent construct that participant r belonging to class c reported for alternative A, B or C in choice task t. The final utility functions were as follows:

Vr,t,A&B|c=1 * consist_hESCr,t,A&B|c+2 * consist_iPSr,t,A&B|c+3 * consist_electricr,t,A&B|c+4 * aim_slowr,t,A&B|c+5 * aim_repairer,t,A&B|c+6 * know_500r,t,A&B|c+7 * know_5000r,t,A&B|c+8 * effect_50r,t,A&B|c+9 * effect_80r,t,A&B|c+10 * sideeffects_0.001r,t,A&B|c+11 * sideeffects_0.01r,t,A&B|c+.

Vr,t,C|c=1 * consist_drugr,t,C|c+2 ** aim_reliefr,t,C|c+3 * know_5000r,t,C|c+4 * effect_50r,t,C|c+5 * sideeffects_0.01r,t,C|c+.

A class assignment model was fitted after the specified utility function. The variables: experience in treatment, side effects, advanced treatment therapy and religious beliefs were tested for their potential impact on class membership in the model. The final class assignment function was:

Vn|c=0+1* treatment_sum|c+2 * experience_sideeffects|c+3 * advanced_treatment|c+4 * Religion_dum|c+.

The relative importance of the attributes included in the DCE was calculated by estimating the difference in preference weights of the latent class model between the most preferred level of an attribute and the least preferred level of the same attribute [21]. The highest difference value was normalized to 1, which represents the most important value. The difference values were divided by the highest value to reveal the relative distance between all other attributes.

We calculated the predicted acceptance uptake for a potential treatment scenario using hESCs to treat patients with PD. Predicted acceptability can be understood as the probability that a participant will accept a described scenario. The scenario represents a hypothetical treatment scenario of treatments with hESCs based on the attributes assessment in the DCE. Attribute estimates assessed by the latent class model were used to calculate the predicted acceptability of attribute levels (treatment with hESCs, risk of severe side effects is 1 out of 1000 and 50 patients received treatment) in relevant future scenarios; (A) effect on symptoms is 2 out of 10, (B) effect on symptoms is 5 out of 10, and (C) effect on symptoms is 8 out of 10.

The predicted acceptability is presented as the percentage of 100 who would accept the presented scenario. The utility for the specific scenario was calculated by using the following equation:

VScenario 1=A+B+C.

The predicted acceptability, the probability of accepting a specific scenario, was then calculated by using the following equation:

Predicted acceptance uptake=1/(1+expVScenario 1).

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Patients accept therapy using embryonic stem cells for Parkinson's ... - BMC Medical Ethics

DNA aptamer finds novel application in regulating cell differentiation – Science Daily

By Embryonic Stem Cells

Generating specific cell lineages from induced pluripotent stem cells and embryonic stem cells is the holy grail of regenerative medicine. Guiding iPSCs toward a target cell line has garnered much attention, but the process remains challenging. Now, researchers from Japan have discovered that an anti-nucleolin DNA aptamer, iSN04, can determine a cell's lineage during differentiation. By demonstrating the generation of cardiomyocytes from murine pluripotent stem cells, their concept shows promise as a regenerative therapy.

Self-renewal and pluripotency-the capacity to form any cell lineage-are inherent characteristics of induced pluripotent stem cells (iPSCs). Furthermore, they are highly prized in regenerative therapies targeting cardiovascular, neurological, and metabolic diseases as they are immunologically suitable for transplantation back into a donor. Unfortunately, regenerative medicine is not yet feasible outside a laboratory setting as available protocols to generate target cells are complicated and expensive. This raises a pertinent question: Can regulating the fate of stem cells in clinical settings and at scale be made more economical?

A team of researchers from Shinshu University, the National Institute of Advanced Industrial Science and Technology, and the University of Shizuoka in Japan set out to address this question by leveraging nucleic acid aptamers. Aptamers are single-stranded pieces of DNA that bind to target proteins and are able to modulate signaling cascades during cell differentiation when a stem cell commits to a specific functional role or phenotype. They hold promise in regenerative medicine as they are easily modified, can be synthesized economically, and are suitable for long-term storage.

The team, led by Associate Professor Tomohide Takaya from the Department of Agricultural and Life Sciences at Shinshu University, recently discovered that an anti-nucleolin aptamer, myogenetic oligodeoxynucleotide iSN04, induced myocardial differentiation in embryonic stem cells (ESCs). The study was led by Mina Ishioka, a graduate student in Dr. Takaya's laboratory, and published in The International Journal of Molecular Sciences on 21 September 2023.

"We had previously found that iSN04 promoted myogenic precursor cells (myoblasts) to differentiate into skeletal muscle cells and had hypothesized that the aptamer also enhanced differentiation of pluripotent stem cells. We were intrigued by the prospect of using iSN04 to promote iPSC differentiation into cardiomyocytes as this could lead to regenerating heart tissue," says Dr. Takaya, elaborating on the team's motivation to pursue the research.

Using various assays like RNA sequencing, cell staining and imaging, and molecular interaction and pathway analysis, the researchers investigated iSN04's effect on murine ESCs and iPSCs. iSN04 treatment under differentiating conditions inhibited stem cell commitment to the cardiac lineage. However, when these pluripotent stem cells were treated after experiencing differentiating conditions for five days, specific marker genes were upregulated, and the cells committed to forming beating cardiomyocytes.

"Ours is the first report to confirm a DNA aptamer that allows cardiomyocytes to develop from iPSCs," explains Dr. Takaya when asked about the significance of the work. "We uncovered two mechanisms of nucleolin interference with iSN04 at play whereby early treatment inhibits cardiomyogenesis, while treatment at a later stage enhances the generation of cardiac progenitors. First, iSN04 governs the translocation of nucleolin protein between the cytoplasm, plasma membrane, and nucleus. Second, it results in the modulation of the Wnt signaling pathway that governs cell differentiation."

The immunostaining experiments revealed that nucleolin was retained in the nucleoli following iSN04 treatment. Nucleolar nucleolin has a role in chromatin remodeling and gene transcription, and interestingly enough, Wnt pathway genes were differentially expressed in the RNA-seq data following iSN04 suppression. The team postulates that the iSN04-anchored nucleolin alters gene expression and Wnt signaling. Ultimately, terminal cell differentiation commits to the cardiomyocyte lineage.

And how could these findings impact regenerative medicine and patients' lives in the long term? Dr. Takaya provides insights into the broader implications of their work. "We believe there is a strong case to be made for further studies evaluating DNA aptamers in regenerative medicine. Aptamers are cost-effective and open up the possibility of producing specific cells from the patient's stem cells. But it doesn't end there! Since the aptamers can regulate stem cell fate, they can serve as therapeutic agents for many conditions linked to stem cell dysfunction," he concludes.

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DNA aptamer finds novel application in regulating cell differentiation - Science Daily

Automation is the key to future advances, says the Head of the … – National Health Executive

By Embryonic Stem Cells

Below, Lee highlights how his passion for stem cell research brought him to the UKSCB, its role in the development of advanced therapeutics and discusses how automation and machine learning in the manufacturing process could mean faster patient access to stem cell therapies in the future.

My career has been hugely varied, taking me around the world across the public, private and academic sectors, but one thing has remained constant: my fascination with cell and gene therapies, and through research, bringing these closer to patients.

Ive been lucky enough to study cell and gene therapies in many different ways over the last twenty years, from the potential of stem cells as a therapy for Type I Diabetes and the role of genes in the cancer cell cycle, to perfecting techniques that differentiate stem cells into precursors for adult blood cells for transfusion therapies. But theres always more to discover.

One application that excites me the most is the use of stem cells for immuno-oncology, where immune cells are used to treat cancer. Were now able to take immune cells from donors and use gene modification so they can fight cancer in patients where they would normally be rejected.

These stem cells can provide an unlimited supply of cancer-fighting T cells, available off-the-shelf to treat anyone quickly when referred to a clinic. This living drug has the potential to constantly adapt to cancer, combating resistance, and existing within patients to continue fighting the disease indefinitely.

In fact, this simplest form of cell can become almost any cell type for many applications in cell therapies, and the UKs national repository for all human embryonic stem cell lines derived within the UK, is here, at the MHRAs UK Stem Cell Bank.

Established 20 years ago to curate all human embryonic stem cells created in the UK and to regulate their use and provision for research and in the clinic, the UKSCB at the MHRAs South Mimms Laboratories is now recognised globally as a leading repository with over 180 different human embryonic stem cell lines.

During this time, weve received support from the Medical Research Council (MRC) and National Institute for Health Research (NIHR) to deliver our banking activities and, together with the lines available for research, weve banked over 30 lines now available for clinical application, making the UKSCB the largest source of clinical grade human embryonic stem cells in the world.

As part of the MHRA, the UKSCB plays an important role in providing regulatory guidance and workshops to the stem cell health sector across the UK, and weve established links with Harvard University to support international training programmes.

The UKSCB is a unique asset within the MHRA, sitting at the forefront of the advanced therapies landscape in the UK, and Im eager to build on its great history and legacy at a time where were only just starting to realise the full potential and application of advanced cell therapies.

As an aging population, we can expect to receive novel treatments for disease in the form of small molecules and prescription drugs but often these only serve to manage symptoms or limit disease progression. Using stem cells, we can now develop replacement joints, neurons for improved brain function and even entire hearts can be made in the lab without the need for human donors.

But what can we do to keep up with demand and ensure quality? The UKSCB has contributed to over 100 publications in international peer reviewed journals, establishing strong links with the World Health Organisation and international partners to improve standards and controls. Weve recently completed a successful international collaboration with Japanese partners, Sinfonia, to trial their automated cell processing robot and we aim to continue with these efforts towards the cost-effective scaling up of our manufacturing.

I believe that future improvements in customer service lies in automation. Reliance on scientists for the manufacture of stem cells is labour intensive, expensive and introduces human error. Automation will alleviate a lot of the manual aspects of cell culture, allowing us to scale-up manufacturing, drive down costs and ensure the highest quality and consistency, allowing stem cell-derived advanced therapies to be more accessible to patients and affordable for healthcare systems. By introducing automation into this process, we can free-up capacity for our leading experts to move away from labour-intensive manufacturing and instead work on improving and adapting novel advanced therapies.

When I look at my role as the third custodian of the UKSCB I hope to build on the significant past achievements of Glyn Stacey and Elsa Abranches that have established us as the leading stem cell institution in the UK today. I hope to move us forward with a sustainable business model that secures the long-term viability of the UKSCB. This means expanding our operations across various cell therapy platforms, supporting the development of specific cell types for use in human cell therapies, and developing new standards, enabling regulatory approval and eventual uptake by healthcare organisations.

I also want the UKSCB to continue making an impact around the world, supporting the development of stem cell therapies and reference materials for advanced therapies with the World Health Organisation. Over the last twenty years, weve delivered more than 370 cell line vials to 25 different countries for research, and in 2022, 54% of the stem cell lines requested have been of clinical grade, rising year on year. I want to see this trend continue as demand for these lines as starting materials for cell therapies grows further.

The future of the MHRAs UKSCB is diverse and exciting and, much like the cells we curate, there are endless possibilities for us to support research and clinical advances in the UK and around the world. I cannot wait to see where the next 20 years will take us.

Image credit: iStock

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Automation is the key to future advances, says the Head of the ... - National Health Executive