Biology subjects

Michael, S.

Publications and source records attributed to Michael, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Improving Growth Predictions in Aquaculture through an Improved Bioenergetics Model Incorporating Feed Composition and Nutrient Digestibility for Largemouth Bass (Micropterus salmoides)

Bioenergetics models serve as mechanistic tools to predict growth by linking energy intake, metabolic expenditure, and nutrient partitioning. However, traditional models rely primarily on gross energy (GE) intake, thereby oversimplifying the effects of feed composition and nutrient availability on fish growth. This work therefore proposed a refined bioenergetics model incorporating nutrient-specific digestibility coefficients (ADCs) and feed composition and tested using a compiled dataset (n=235; 165 for calibration and 70 for independent validation) and a field experimental dataset of largemouth bass (Micropterus salmoides). We first optimized parameters of a gross energy intake-based bioenergetics model, increasing R2 from 0.62 to 0.96 and thereby providing a calibrated foundation for subsequent refined model. The refined model demonstrated superior predictive performance on the compiled dataset (R2 = 0.97) with RMSE = 19.86 g and MAE = 10.31 g), representing reductions of 4.13% in RMSE and 19.98% in MAE and a 1.03% increase in R2 compared with the optimized GE-based model. In the field experiment, the refined model achieved high predictive accuracy (R2 = 0.98 and 0.97), whereas the optimized GE-based model showed poor performance (R2 = 0.33 and 0.06 respectively). This study is, to our knowledge, the first bioenergetics framework for largemouth bass that decomposes feed composition and nutrient-specific ADCs to compute macronutrient-resolved digestible energy, enabling formulation-aware growth prediction and nutrient-oriented optimization.

bioengineering↗

bioRxiv2024

Metabolic insights into microbially induced calcite formation by Bacillaceae for application in bio-based construction materials

Microbially induced calcite precipitation (MICP) offers promising solutions for sustainable, low-cement infrastructure materials. While it is known how urea catabolism leads to biomineralization, the non-ureolytic pathways of MICP are less clear. This limits the use of the latter in biotechnology, despite its clear benefit of avoiding toxic ammonia release. To address this knowledge gap, the present study explored the interdependence between carbon source utilization and non-ureolytic MICP. We show that acetate can serve as the carbon source driving calcite formation in several environmental Bacillaceae isolates. This effect was particularly clear in a Solibacillus silvestris strain, which could precipitate almost all provided calcium when provided with a 2:1 acetate-to-calcium molar ratio, and we show that this process was independent of active cell growth. Genome sequencing and gene expression analyses revealed an apparent link between acetate catabolism and calcite precipitation in this species, suggesting MICP may be a calcium stress response. Development of a simple genetic system for S. silvestris led to deletion of a proposed calcium binding protein, although this showed minimal effects on MICP. Taken together, this study provides insights into the physiological processes leading to non-ureolytic MICP, paving the way for targeted optimization of biomineralization for sustainable materials development.

microbiology↗

bioRxiv2024

Cardiac p16 Expression Following Vape Exposure with Nicotine Shows Sex-Specific Induction in Males but not Females

Vaping is marketed as a safe alternative to traditional cigarette smoking, but multiple studies demonstrate deleterious cardiopulmonary effects including cardiac function decline and fibrotic remodeling with alveolar size enlargement. Nicotine, a common constituent of vaping aerosol, stimulates p16 expression in pulmonary tissue but the impact on cardiac tissue remains unclear. In this study, mice were exposed to e-cigarette vape aerosol either containing nicotine (Vape Nicotine; VN) or without nicotine (Vape 0; V0). Non-exposed (No Vape; NoV) mice were used as controls. Cardiac effects were assessed by echocardiography, histology, and immunofluorescence to determine changes in function, morphology, p16, and Discoidin Domain Receptor 2 (DDR2). VN depressed cardiac function and increased collagen deposition relative to V0 and NoV. Interestingly, p16 expression was increased in cardiomyocytes and interstitial cells of male mice while remaining unchanged in females. In contrast to VN, V0 had no significant impact on cardiac function or p16 expression in males. Furthermore, collagen deposition in the V0 group was significantly lower than the VN group. Subsequent cardiac fibroblast analysis using DDR2 revealed increased expression within the V0 group relative to VN and NoV. Collectively, these findings show collagen accumulation as well as p16 expression prompted by vaping is mediated by nicotine as a constituent of vape juice. In contrast, vape aerosol alone promotes accrual of cardiac fibroblasts without concomitant changes in collagen accumulation or p16 expression. These results are the first to identify p16 induction with pathologic collagen deposition by exposure to vape aerosol containing nicotine in male cardiac tissue. The underlying basis for sex-specific differences in cardiac responses to vape aerosol exposure warrant further investigation, particularly those involving cellular and molecular changes that may lead to pathologic changes later in life.

molecular biology↗

bioRxiv2023

Selective refueling of CAR T cells using ADA1 and 1 CD26 boosts antitumor immunity

CAR T cell therapy has revolutionized the treatment of hematologic malignancies, but its efficacy in solid tumors remains limited due to the immunosuppressive nature of the tumor microenvironment and the inability of T cells to persist and traffic to the tumor site. While current strategies focus on enhancing CAR T cell activity through costimulatory molecules and cytokines, a critical yet often overlooked factor is the competition for nutrients between tumor cells and T cells in the nutrient-deprived tumor microenvironment. To address this challenge, we employed a selective metabolic refueling (MR) strategy by providing T cells with inosine as an alternative fuel source for growth and functionality. In this study, we engineered CAR T cells to co-express a membrane-bound CD26 and a cytoplasmic adenosine deaminase 1 (ADA1) fused to an anti-CD3 scFv. ADA1 irreversibly converts both intracellular and extracellular adenosine to inosine, overcoming adenosine-mediated immunosuppression and providing T cells with inosine for growth. The inclusion of an anti-CD3 scFv fusion partner and overexpressing CD26 boosts ADA1 capture in a membrane proximal manner, providing inosine for T cells and minimizing feeding the tumor cells. We demonstrate that ADA1 is conditionally secreted only in stress conditions and that it activates CAR T cells through trans-signaling in a tumor-specific manner. In addition, we show that, compared to unmodified CAR T cells, CD26-overexpressing CAR T cells have better migration capacity and are less susceptible to TGF-{beta}1 suppression. Finally, we found that, in mice models of human hepatocellular carcinoma (GPC3-MR-CAR) and human non-small cell lung cancer (HER2-MR-CAR), metabolically refueled CAR T cells (MR-CAR) are more efficient in reducing tumor growth than unmodified CAR T cells. Thus, selective refueling CAR T cells using ADA1 and CD26 holds promise for improving the efficacy of CAR T cell therapy of solid tumors.

immunology↗

bioRxiv2023

Locus specific human endogenous retroviruses reveal new lymphoma subtypes

The heterogeneity of cancers are driven by diverse mechanisms underlying oncogenesis such as differential cell-of-origin (COO) progenitors, mutagenesis, and viral infections. Classification of B-cell lymphomas have been defined by considering these characteristics. However, the expression and contribution of transposable elements (TEs) to B cell lymphoma oncogenesis or classification have been overlooked. We hypothesized that incorporating TE signatures would increase the resolution of B-cell identity during healthy and malignant conditions. Here, we present the first comprehensive, locus-specific characterization of TE expression in benign germinal center (GC) B-cells, diffuse large B-cell lymphoma (DLBCL), Epstein-Barr virus (EBV)-positive and EBV-negative Burkitt lymphoma (BL), and follicular lymphoma (FL). Our findings demonstrate unique human endogenous retrovirus (HERV) signatures in the GC and lymphoma subtypes whose activity can be used in combination with gene expression to define B-cell lineage in lymphoid malignancies, highlighting the potential of retrotranscriptomic analyses as a tool in lymphoma classification, diagnosis, and the identification of novel treatment groups.

cancer biology↗

bioRxiv2022

Near-chromosomal de novo assembly of Bengal tiger genome reveals genetic hallmarks of apex-predation

The tiger, a poster child for conservation, remains an endangered apex predator. Continued survival and recovery will require a comprehensive understanding of their genetic diversity and the use of such information for population management. A high-quality tiger genome assembly will be an important tool for conservation genetics, especially for the Indian tiger, the most abundant subspecies in the wild. Here, we present high-quality near-chromosomal genome assemblies of a female and a male wild Indian tiger (Panthera tigris tigris). Our assemblies had a scaffold N50 of >140{square}Mb, with 19{square}scaffolds, corresponding to the 19 numbered chromosomes, containing 95% of the genome. Our assemblies also enabled detection of longer stretches of runs of homozygosity compared to previous assemblies which will improve estimates of genomic inbreeding. Comprehensive genome annotation identified 26,068 protein-coding genes, including several gene families involved in key morphological features such as the teeth, claws, vision, olfaction, taste and body stripes. We also identified 301 microRNAs, 365 small nucleolar RNAs, 632 tRNAs and other noncoding RNA elements, several of which are predicted to regulate key biological pathways that likely contribute to tigers apex predatory traits. We identify signatures of positive selection in the tiger genome that are consistent with the Panthera lineage. Our high-quality genome will enable use of non-invasive samples for comprehensive assessment of genetic diversity, thus supporting effective conservation and management of wild tiger populations.

genomics↗

bioRxiv2022

A Comprehensive Roadmap of Human Placental Development in vitro

Human pluripotent stem cells (hPSCs) represent a powerful model system to study early developmental processes. However, lineage specification into trophectoderm (TE) remains poorly understood and access to well-characterized placental cells for biomedical research is limited, largely depending on fetal tissues or cancer cell lines. Here, we developed novel strategies enabling highly efficient TE specification that generates cytotrophoblast (CTB) and multinucleated primary syncytiotrophoblast (STB) followed by establishment of trophoblast stem cells (TSCs) capable of differentiating into extravillous trophoblast (EVT) and STB after long-term expansion. We confirmed stepwise induction of lineage- and cell-type-specific genes and substantiated typical features of placental cells using morphological, biochemical, integrated multi-omics, and single-cell analyses. Our data provide conclusive evidence that conventional hPSCs can be directly and exclusively converted into TE, thereby providing an unlimited source of diverse placental cell types suitable for a broad range of biomedical applications.

developmental biology↗

bioRxiv2022

A real-time cellular thermal shift assay (RT-CETSA) to monitor target engagement

Determining a molecules mechanism of action is paramount during chemical probe development and drug discovery. The cellular thermal shift assay (CETSA) is a valuable tool to confirm target engagement in cells for a small molecule that demonstrates a pharmacological effect. CETSA directly detects biophysical interactions between ligands and protein targets, which can alter a proteins unfolding and aggregation properties in response to thermal challenge. In traditional CETSA experiments, each temperature requires an individual sample, which restricts throughput and requires substantial optimization. To capture the full aggregation profile of a protein from a single sample, we developed a prototype real-time CETSA (RT-CETSA) platform by coupling a real-time PCR instrument with a CCD camera to detect luminescence. A thermally stable Nanoluciferase variant (ThermLuc) was bioengineered that withstood unfolding at temperatures greater than 90 degrees Celsius and was compatible with monitoring target engagement events when fused to diverse targets. Utilizing well-characterized inhibitors of lactate dehydrogenase alpha, RT-CETSA showed significant correlation with enzymatic, biophysical, and other cell-based assays. A data analysis pipeline was developed to enhance the sensitivity of RT-CETSA to detect on-target binding. The RT-CETSA technology advances capabilities of the CETSA method and facilitates the identification of ligand-target engagement in cells, a critical step in assessing the mechanism of action of a small molecule. SignificanceValidating target engagement is a critical step when characterizing a small molecule modulator. The cellular thermal shift assay (CETSA) is a common approach to examine target engagement, as alterations in the thermal stability of a protein can be conferred by ligand binding. An advantage of CETSA is that it does not require modification of the protein target or small molecule. Major limitations are the throughput and ease-of-use, as the traditional detection method uses western blots, which limits the number of samples that can be processed. Higher-throughput CETSA methods have been developed but are performed at a single temperature and require target-specific optimization. We developed a high-throughput real-time CETSA to circumvent these challenges, providing a rapid and cost-effective strategy to assess on-target activity of a small molecule in living cells.

pharmacology and toxicology↗

bioRxiv2021

A high throughput screening assay for inhibitors of SARS-CoV-2 pseudotyped particle entry

Effective small molecule therapies to combat the SARS-CoV-2 infection are still lacking as the COVID-19 pandemic continues globally. High throughput screening assays are needed for lead discovery and optimization of small molecule SARS-CoV-2 inhibitors. In this work, we have applied viral pseudotyping to establish a cell-based SARS-CoV-2 entry assay. Here, the pseudotyped particles (PP) contain SARS-CoV-2 spike in a membrane enveloping both the murine leukemia virus (MLV) gag-pol polyprotein and luciferase reporter RNA. Upon addition of PP to HEK293-ACE2 cells, the SARS-CoV-2 spike protein binds to the ACE2 receptor on the cell surface, resulting in priming by host proteases to trigger endocytosis of these particles, and membrane fusion between the particle envelope and the cell membrane. The internalized luciferase reporter gene is then expressed in cells, resulting in a luminescent readout as a surrogate for spike-mediated entry into cells. This SARS-CoV-2 PP entry assay can be executed in a biosafety level 2 containment lab for high throughput screening. From a collection of 5,158 approved drugs and drug candidates, our screening efforts identified 7 active compounds that inhibited the SARS-CoV-2-S PP entry. Of these seven, six compounds were active against live replicating SARS-CoV-2 virus in a cytopathic effect assay. Our results demonstrated the utility of this assay in the discovery and development of SARS-CoV-2 entry inhibitors as well as the mechanistic study of anti-SARS-CoV-2 compounds. Additionally, particles pseudotyped with spike proteins from SARS-CoV-2 B.1.1.7 and B.1.351 variants were prepared and used to evaluate the therapeutic effects of viral entry inhibitors.

pharmacology and toxicology↗

bioRxiv2021

Directed Differentiation of Human Pluripotent Stem Cells into Radial Glia and Astrocytes Bypasses Neurogenesis

Derivation of astrocytes from human pluripotent stem cells (hPSCs) is inefficient and cumbersome, impeding their use in biomedical research. Here, we developed a highly efficient chemically defined astrocyte differentiation strategy that overcomes current limitations. This approach largely bypasses neurogenesis, which otherwise precedes astrogliogenesis during brain development and in vitro experiments. hPSCs were first differentiated into radial glial cells (RGCs) exhibiting in vivo-like radial glia signatures. Activation of NOTCH and JAK/STAT pathways in bona fide RGCs resulted in direct astrogliogenesis confirmed by expression of various glial markers (NFIA, NFIB, SOX9, CD44, S100B, GFAP). Transcriptomic and genome-wide epigenetic analyses confirmed RGC-to-astrocyte differentiation and absence of neurogenesis. The morphological and functional identity of hPSC-derived astrocytes was confirmed by using an array of methods (e.g. electron microscopy, calcium imaging, co-culture with neurons, grafting into mouse brains). Lastly, the scalable protocol was adapted to a robotic platform and used to model Alexander disease. In conclusion, our findings uncover remarkable plasticity in neural lineage progression that can be exploited to manufacture large numbers of human hPSC-derived astrocytes for drug development and regenerative medicine.

developmental biology↗

bioRxiv2021

Cross-platform Bayesian optimization system for autonomous biological assay development

Current high-throughput screening assay optimization is often a manual and time-consuming process, even when utilizing design-of-experiment approaches. A cross-platform, Cloud-based Bayesian optimization-based algorithm was developed as part of the NCATS ASPIRE Initiative to accelerate preclinical drug discovery. A cell-free assay for papain enzymatic activity was used as proof-of-concept for biological assay development. Compared to a brute force approach that sequentially tested all 294 assay conditions to find the global optimum, the Bayesian optimization algorithm could find suitable conditions for optimal assay performance by testing only 21 assay conditions on average, with up to 20 conditions being tested simultaneously. The algorithm could achieve a seven-fold reduction in costs for lab supplies and high-throughput experimentation run-time, all while being controlled from a remote site through a secure connection. Based on this proof-of-concept, this technology is expected to be applied to more complex biological assays and automated chemistry reaction screening at NCATS, and should be transferable to other institutions.

biochemistry↗

bioRxiv2021

Retro Drug Design: From Target Properties to Molecular Structures

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.

bioinformatics↗

bioRxiv2020

Robotic High-Throughput Biomanufacturing and Functional Differentiation of Human Pluripotent Stem Cells

Efficient translation of human induced pluripotent stem cells (hiPSCs) depends on implementing scalable cell manufacturing strategies that ensure optimal self-renewal and functional differentiation. Currently, manual culture of hiPSCs is highly variable and labor-intensive posing significant challenges for high-throughput applications. Here, we established a robotic platform and automated all essential steps of hiPSC culture and differentiation under chemically defined conditions. This streamlined approach allowed rapid and standardized manufacturing of billions of hiPSCs that can be produced in parallel from up to 90 different patient-and disease-specific cell lines. Moreover, we established automated multi-lineage differentiation to generate primary embryonic germ layers and more mature phenotypes such as neurons, cardiomyocytes, and hepatocytes. To validate our approach, we carefully compared robotic and manual cell culture and performed molecular and functional cell characterizations (e.g. bulk culture and single-cell transcriptomics, mass cytometry, metabolism, electrophysiology, Zika virus experiments) in order to benchmark industrial-scale cell culture operations towards building an integrated platform for efficient cell manufacturing for disease modeling, drug screening, and cell therapy. Combining stem cell-based models and non-stop robotic cell culture may become a powerful strategy to increase scientific rigor and productivity, which are particularly important during public health emergencies (e.g. opioid crisis, COVID-19 pandemic).

bioengineering↗

bioRxiv2020

Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro

COVID-19 is undoubtedly the most impactful viral disease of the current century, afflicting millions worldwide. As yet, there is not an approved vaccine, as well as limited options from existing drugs for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Overall, we identified 16 synergistic and 8 antagonistic combinations, 4 of which were both synergistic and antagonistic in a dose-dependent manner. Among the 16 synergistic cases, combinations of nitazoxanide with three other compounds (remdesivir, amodiaquine and umifenovir) were the most notable, all exhibiting significant synergy against SARS-CoV-2. The combination of nitazoxanide, an FDA-approved drug, and remdesivir, FDA emergency use authorization for the treatment of COVID-19, demonstrate a strong synergistic interaction. Notably, the combination of remdesivir and hydroxychloroquine demonstrated strong antagonism. Overall, our results emphasize the importance of both drug repurposing and preclinical testing of drug combinations for potential therapeutic use against SARS-CoV-2 infections.

microbiology↗

bioRxiv2020

An OpenData portal to share COVID-19 drug repurposing data in real time

The National Center for Advancing Translational Sciences (NCATS) has developed an online open science platform - named the NCATS OpenData Portal (ODP) - for quickly and freely sharing complete NCATS translational datasets via an open-access, user-friendly interface. This paper describes the establishment of the ODP, initially deployed during the COVID-19 crisis, and provides a detailed analysis of COVID-19 drug repurposing screening datasets that served as the first large-scale use case for the platform. Over 10,000 compounds were tested across 17 quantitative high-throughput assays, covering a wide spectrum of the SARS-CoV-2 life cycle. In total, over 87,000 concentration-response curves and 426,000 data points were made publicly available on ODP in near real-time, enabling immediate access to complete datasets. The resource is flexible in accommodating various types and structures of data, and it has already expanded since its launch to host additional datasets for COVID-19, other viruses of pandemic potential, and beyond. The OpenData Portal has been designed as a scalable platform for real-time data sharing across drug discovery campaigns, regardless of disease area, with the overarching goal of accelerating discovery at NCATS, the NIH, and the greater scientific community.

microbiology↗

bioRxiv2020

The SARS-CoV-2 cytopathic effect is blocked with autophagy modulators

SARS-CoV-2 is a new type of coronavirus capable of rapid transmission and causing severe clinical symptoms; much of which has unknown biological etiology. It has prompted researchers to rapidly mobilize their efforts towards identifying and developing anti-viral therapeutics and vaccines. Discovering and understanding the virus’ pathways of infection, host-protein interactions, and cytopathic effects will greatly aid in the design of new therapeutics to treat COVID-19. While it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic. Therefore, we evaluated a range of other autophagy modulators to identify an alternative autophagy-based drug repurposing opportunity. In this work, we found that 6 of these compounds blocked the cytopathic effect of SARS-CoV-2 in Vero-E6 cells with EC50 values ranging from 2.0 to 13 µM and selectivity indices ranging from 1.5 to >10-fold. Immunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay. Our data suggest that autophagy pathways could be targeted to combat SARS-CoV-2 infections and become an important component of drug combination therapies to improve the treatment outcomes for COVID-19.One Sentence Summary Blocking SARS-CoV-2 cytopathic effects with selective autophagy inhibitors underlying the clinical benefits of chloroquine and hydroxychloroquine.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗

bioRxiv2020

An Integrated Systems Biology Approach Identifies the Proteasome as a Critical Host Machinery for ZIKV and DENV Replication

The Zika (ZIKV) and dengue (DENV) flaviviruses exhibit similar replicative processes but distinct clinical outcomes. A systematic understanding of virus-host protein-protein interaction networks can reveal cellular pathways critical to viral replication and disease pathogenesis. Here we employed three independent systems biology approaches toward this goal. First, protein array analysis of direct interactions between individual ZIKV/DENV viral proteins and 20,240 human proteins revealed multiple conserved cellular pathways and protein complexes, including proteasome complexes. Second, an RNAi screen of 10,415 druggable genes to identify host proteins required for ZIKV infection uncovered proteasome proteins. Third, a high-throughput screening of 6,016 bioactive compounds for ZIKV inhibitors yielded 134 effective compounds, including six proteasome inhibitors that suppress both ZIKV and DENV replication. Integrative analyses of these orthogonal datasets pinpoints proteasome as critical host machinery for ZIKV/DENV replication. Our study provides multi-omics datasets for further studies of flavivirus-host interactions, disease pathogenesis, and new drug targets.

microbiology↗

bioRxiv2019

A Versatile Polypharmacology Platform Promotes Cytoprotection and Viability of Human Pluripotent and Differentiated Cells

Clinical translation of human pluripotent stem cells (hPSCs) requires advanced strategies that ensure safe and robust long-term growth and functional differentiation. Pluripotent cells are capable of extensive self-renewal, yet remain highly sensitive to environmental perturbations in vitro, posing challenges to their therapeutic use. Here, we deployed innovative high-throughput screening strategies to identify a small molecule cocktail that dramatically improves viability of hPSCs and their differentiated progeny. We discovered that the combination of Chroman 1, Emricasan, Polyamines, and Trans-ISRIB (CEPT) enhanced cell survival of genetically stable hPSCs by simultaneously blocking several stress mechanisms that otherwise compromise cell structure and function. In proof-of-principle experiments we then demonstrated the strong improvements that CEPT provided for several key applications in stem cell research, including routine cell passaging, cryopreservation of pluripotent and differentiated cells, embryoid body and organoid formation, single-cell cloning, genome editing, and new iPSC line generation. Thus, CEPT represents a unique polypharmacology strategy for comprehensive cytoprotection, providing a new rationale for efficient and safe utilization of hPSCs. Conferring cell fitness by multi-target drug combinations may become a common approach in cryobiology, drug development, and regenerative medicine.

cell biology↗