Generation of induced pluripotent stem cell line (MIPTi002-A) derived from a patient with a heterozygous type mutation in the CDC73 gene

CDC73-related disorders are inherited in an autosomal dominant manner. An individual with a CDC73-related disorder may have inherited the disorder from an affected parent or developed it as the result of a de novo pathogenic variant of CDC73. The iPSC line was obtained by reprogramming the PBMCs of a patient with a heterozygous type mutation of the CDC73 gene. This cell line could be useful to scrutinize and study the development of CDC73-associated parathyroid carcinoma.

A transcriptomic taxonomy of mouse brain-wide spinal projecting neurons

The brain controls nearly all bodily functions via spinal projecting neurons (SPNs) that carry command signals from the brain to the spinal cord. However, a comprehensive molecular characterization of brain-wide SPNs is still lacking. Here we transcriptionally profiled a total of 65,002 SPNs, identified 76 region-specific SPN types, and mapped these types into a companion atlas of the whole mouse brain1. This taxonomy reveals a three-component organization of SPNs: (1) molecularly homogeneous excitatory SPNs from the cortex, red nucleus and cerebellum with somatotopic spinal terminations suitable for point-to-point communication; (2) heterogeneous populations in the reticular formation with broad spinal termination patterns, suitable for relaying commands related to the activities of the entire spinal cord; and (3) modulatory neurons expressing slow-acting neurotransmitters and/or neuropeptides in the hypothalamus, midbrain and reticular formation for 'gain setting' of brain-spinal signals. In addition, this atlas revealed a LIM homeobox transcription factor code that parcellates the reticulospinal neurons into five molecularly distinct and spatially segregated populations. Finally, we found transcriptional signatures of a subset of SPNs with large soma size and correlated these with fast-firing electrophysiological properties. Together, this study establishes a comprehensive taxonomy of brain-wide SPNs and provides insight into the functional organization of SPNs in mediating brain control of bodily functions.

Generation of two human induced pluripotent stem cell lines (ABi001-A and ABi002-A) from cone dystrophy with supernormal rod response patients caused by KCNV2 mutation

Cone dystrophy with supernormal rod response (CDSRR) is associated with pathogenic variants of the KCNV2 gene that result in severe symptoms, including color vision defects, decreased visual acuity, and specific changes in electroretinogram responses. Two iPSC lines were obtained from two patients in the same family with different types of mutations in the KCNV2 gene. These lines could serve as a useful model for studying the pathogenetic mechanism and treatment development for CDSRR. PBMCs from donors have been reprogrammed into iPSC lines. Derived clones were characterized with mutation sequencing, analysis of common pluripotency-associated markers at the protein levels, and in vitro differentiation studies.

SARS-CoV-2-specific T cells and antibodies in COVID-19 protection: a prospective study

Background

During the ongoing coronavirus disease 2019 (COVID-19) pandemic, many individuals were infected with and have cleared the virus, developing virus-specific antibodies and effector/memory T cells. An important unanswered question is what levels of T-cell and antibody responses are sufficient to protect from the infection.

Methods

In 5340 Moscow residents, we evaluated anti–severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin M (IgM)/immunoglobulin G (IgG) titers and frequencies of the T cells specific to the membrane, nucleocapsid, and spike proteins of SARS-CoV-2, using interferon gamma (IFN-γ) enzyme-linked immunosorbent spot (ELISpot) assay. Additionally, we evaluated the fractions of virus-specific CD4⁺ and CD8⁺ T cells using intracellular staining of IFN-γ and interleukin 2 followed by flow cytometry. We analyzed the COVID-19 rates as a function of the assessed antibody and T-cell responses, using the Kaplan–Meier estimator method, for up to 300 days postinclusion.

Results

We showed that T-cell and antibody responses are closely interconnected and are commonly induced concurrently. Magnitudes of both responses inversely correlated with infection probability. Individuals positive for both responses demonstrated the highest levels of protectivity against the SARS-CoV-2 infection. A comparable level of protection was found in individuals with antibody response only, whereas the T-cell response by itself granted only intermediate protection.

Conclusions

We found that the contribution of the virus-specific antibodies to protection against SARS-CoV-2 infection is more pronounced than that of the T cells. The data on the virus-specific IgG titers may be instructive for making decisions in personalized healthcare and public anti–COVID-19 policies.

Clinical Trials Registration. NCT04898140.

Transplantation of miPSC/mESC-derived retinal ganglion cells into healthy and glaucomatous retinas

Optic neuropathies, including glaucoma, are a group of neurodegenerative diseases, characterized by the progressive loss of retinal ganglion cells (RGCs), leading to irreversible vision loss. While previous studies demonstrated the potential to replace RGCs with primary neurons from developing mouse retinas, their use is limited clinically. We demonstrate successful transplantation of mouse induced pluripotent stem cell (miPSC)/mouse embryonic stem cell (mESC)-derived RGCs into healthy and glaucomatous mouse retinas, at a success rate exceeding 65% and a donor cell survival window of up to 12 months. Transplanted Thy1-GFP+ RGCs were able to polarize within the host retina and formed axonal processes that followed host axons along the retinal surface and entered the optic nerve head. RNA sequencing of donor RGCs re-isolated from host retinas at 24 h and 1 week post-transplantation showed upregulation of cellular pathways mediating axonal outgrowth, extension, and guidance. Additionally, we provide evidence of subtype-specific diversity within miPSC-derived RGCs prior to transplantation.

Semi-Automated Approach for Retinal Tissue Differentiation

Purpose

Three-dimensional strategy for the differentiation of pluripotent stem cells to the retina has been widely used to study retinal development, although the cell production and drug discovery applications are limited by the throughput. Here we attempted to scale up the protocol using a semiautomated approach.

Methods

For the experiments we used the Rx-GFP mouse embryonic stem cell (mES) reporter cell line, specific for early retinal development and human embryonic stem cell line Brn3b-tdTomato, specific for retinal ganglion cells. To increase the throughput, we implemented automated media exchange using Thermo WellWash Versa with Thermo RapidStack robot. To analyze the rate of retinal differentiation in mouse stem-cell derived organoids we imaged the plates at day 10 of differentiation using Life Technologies EVOS Fl Auto. The automated image analysis of fluorescent images was performed with custom Python OpenCV script.

Results

The implementation of a semiautomated approach significantly reduced the operator time needed: 34 minutes versus two hours for 960 organoids over the course of 25 days without any change in differentiation pattern and quantity of retinal differentiation. Automated image analysis showed that Forskolin treatment starting from day 1 leads to a significant increase in retinal field induction efficiency.

Conclusions

Semiautomated approach can be applied to retinal tissue differentiation to increase the throughput of the protocol. We demonstrated that automated image analysis can be used to evaluate differentiation efficiency, as well as for troubleshooting and to study factors affecting retinal differentiation.

Translational Relevance

Using robotic approach reduces the risk of human error and allows to perform all cycle of cell production in enclosed conditions, which is critical for GMP cell manufacture.

Optimizing the Conditions and Use of Synthetic Matrix for Three-Dimensional In Vitro Retinal Differentiation from Mouse Pluripotent Cells

IMPACT STATEMENT

The development of retinal regenerative therapies relies on the reproducible and renewable source of retinal neurons for drug discovery and cell transplantation. Three-dimensional approach for retinal differentiation from pluripotent cells recently emerged as the robust strategy for retinal tissue differentiation. In this work, we present the combination of optimized conditions and techniques for three-dimensional retinal differentiation from mouse embryonic cells that improves reproducibility and efficiency of retinal differentiation in organoid cultures. We also show that the retinal induction can be achieved with the synthetic oligopeptide instead of Matrigel that allows to approach xeno-free conditions for cell production.

Convolutional Neural Networks Can Predict Retinal Differentiation in Retinal Organoids

We have developed a deep learning-based computer algorithm to recognize and predict retinal differentiation in stem cell-derived organoids based on bright-field imaging. The three-dimensional "organoid" approach for the differentiation of pluripotent stem cells (PSC) into retinal and other neural tissues has become a major in vitro strategy to recapitulate development. We decided to develop a universal, robust, and non-invasive method to assess retinal differentiation that would not require chemical probes or reporter gene expression. We hypothesized that basic-contrast bright-field (BF) images contain sufficient information on tissue specification, and it is possible to extract this data using convolutional neural networks (CNNs). Retina-specific Rx-green fluorescent protein mouse embryonic reporter stem cells have been used for all of the differentiation experiments in this work. The BF images of organoids have been taken on day 5 and fluorescent on day 9. To train the CNN, we utilized a transfer learning approach: ImageNet pre-trained ResNet50v2, VGG19, Xception, and DenseNet121 CNNs had been trained on labeled BF images of the organoids, divided into two categories (retina and non-retina), based on the fluorescent reporter gene expression. The best-performing classifier with ResNet50v2 architecture showed a receiver operating characteristic-area under the curve score of 0.91 on a test dataset. A comparison of the best-performing CNN with the human-based classifier showed that the CNN algorithm performs better than the expert in predicting organoid fate (84% vs. 67 ± 6% of correct predictions, respectively), confirming our original hypothesis. Overall, we have demonstrated that the computer algorithm can successfully recognize and predict retinal differentiation in organoids before the onset of reporter gene expression. This is the first demonstration of CNN's ability to classify stem cell-derived tissue in vitro.