A Multiplexed Barcodelet Single-Cell RNA-Seq Approach Elucidates Combinatorial Signaling Pathways that Drive ESC Differentiation

Barcodes based on nucleic acid sequences: Applications and challenges (Review)

Cells are the fundamental structural and functional units of living organisms and the study of these entities has remained a central focus throughout the history of biological sciences. Traditional cell research techniques, including fluorescent protein tagging and microscopy, have provided preliminary insights into the lineage history and clonal relationships between progenitor and descendant cells. However, these techniques exhibit inherent limitations in tracking the full developmental trajectory of cells and elucidating their heterogeneity, including sensitivity, stability and barcode drift. In developmental biology, nucleic acid barcode technology has introduced an innovative approach to cell lineage tracing. By assigning unique barcodes to individual cells, researchers can accurately identify and trace the origin and differentiation pathways of cells at various developmental stages, thereby illuminating the dynamic processes underlying tissue development and organogenesis. In cancer research, nucleic acid barcoding has played a pivotal role in analyzing the clonal architecture of tumor cells, exploring their heterogeneity and resistance mechanisms and enhancing our understanding of cancer evolution and inter‑clonal interactions. Furthermore, nucleic acid barcodes play a crucial role in stem cell research, enabling the tracking of stem cells from diverse origins and their derived progeny. This has offered novel perspectives on the mechanisms of stem cell self‑renewal and differentiation. The present review presented a comprehensive examination of the principles, applications and challenges associated with nucleic acid barcode technology.

Reconstructing signaling histories of single cells via perturbation screens and transfer learning

Manipulating the signaling environment is an effective approach to alter cellular states for broad-ranging applications, from engineering tissues to treating diseases. Such manipulation requires knowing the signaling states and histories of the cells in situ , for which high-throughput discovery methods are lacking. Here, we present an integrated experimental-computational framework that learns signaling response signatures from a high-throughput in vitro perturbation atlas and infers combinatorial signaling activities in in vivo cell types with high accuracy and temporal resolution. Specifically, we generated signaling perturbation atlas across diverse cell types/states through multiplexed sequential combinatorial screens on human pluripotent stem cells. Using the atlas to train IRIS, a neural network-based model, and predicting on mouse embryo scRNAseq atlas, we discovered global features of combinatorial signaling code usage over time, identified biologically meaningful heterogeneity of signaling states within each cell type, and reconstructed signaling histories along diverse cell lineages. We further demonstrated that IRIS greatly accelerates the optimization of stem cell differentiation protocols by drastically reducing the combinatorial space that needs to be tested. This framework leads to the revelation that different cell types share robust signal response signatures, and provides a scalable solution for mapping complex signaling interactions in vivo to guide targeted interventions.

Dual-role transcription factors stabilize intermediate expression levels

Precise control of gene expression levels is essential for normal cell functions, yet how they are defined and tightly maintained, particularly at intermediate levels, remains elusive. Here, using a series of newly developed sequencing, imaging, and functional assays, we uncover a class of transcription factors with dual roles as activators and repressors, referred to as condensate-forming level-regulating dual-action transcription factors (TFs). They reduce high expression but increase low expression to achieve stable intermediate levels. Dual-action TFs directly exert activating and repressing functions via condensate-forming domains that compartmentalize core transcriptional unit selectively. Clinically relevant mutations in these domains, which are linked to a range of developmental disorders, impair condensate selectivity and dual-action TF activity. These results collectively address a fundamental question in expression regulation and demonstrate the potential of level-regulating dual-action TFs as powerful effectors for engineering controlled expression levels.

Antiviral peptides inhibiting the main protease of SARS-CoV-2 investigated by computational screening and in vitro protease assay

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays an important role in viral replication and transcription and received great attention as a vital target for drug/peptide development. Therapeutic agents such as small-molecule drugs or peptides that interact with the Cys-His present in the catalytic site of Mpro are an efficient way to inhibit the protease. Although several emergency-approved vaccines showed good efficacy and drastically dropped the infection rate, evolving variants are still infecting and killing millions of people globally. While a small-molecule drug (Paxlovid) received emergency approval, small-molecule drugs have low target specificity and higher toxicity. Besides small-molecule drugs, peptide therapeutics are thus gaining increasing popularity as they are easy to synthesize and highly selective and have limited side effects. In this study, we investigated the therapeutic value of 67 peptides targeting Mpro using molecular docking. Subsequently, molecular dynamics (MD) simulations were implemented on eight protein-peptide complexes to obtain molecular-level information on the interaction between these peptides and the Mpro active site, which revealed that temporin L, indolicidin, and lymphocytic choriomeningitis virus (LCMV) GP1 are the best candidates in terms of stability, interaction, and structural compactness. These peptides were synthesized using the solid-phase peptide synthesis protocol, purified by reversed-phase high-performance liquid chromatography (RP-HPLC), and authenticated by mass spectrometry (MS). The in vitro fluorometric Mpro activity assay was used to validate the computational results, where temporin L and indolicidin were observed to be very active against SARS-CoV-2 Mpro with IC50 values of 38.80 and 87.23 μM, respectively. A liquid chromatography-MS (LC-MS) assay was developed, and the IC50 value of temporin L was measured at 23.8 μM. The solution-state nuclear magnetic resonance (NMR) structure of temporin L was determined in the absence of sodium dodecyl sulfate (SDS) micelles and was compared to previous temporin structures. This combined investigation provides critical insights and assists us to further develop peptide inhibitors of SARS-CoV-2 Mpro through structural guided investigation.

In vitro and in vivo models define a molecular signature reference for human embryonic notochordal cells

Understanding the emergence of human notochordal cells (NC) is essential for the development of regenerative approaches. We present a comprehensive investigation into the specification and generation of bona fide NC using a straightforward pluripotent stem cell (PSC)-based system benchmarked with human fetal notochord. By integrating in vitro and in vivo transcriptomic data at single-cell resolution, we establish an extended molecular signature and overcome the limitations associated with studying human notochordal lineage at early developmental stages. We show that TGF-β inhibition enhances the yield and homogeneity of notochordal lineage commitment in vitro. Furthermore, this study characterizes regulators of cell-fate decision and matrisome enriched in the notochordal niche. Importantly, we identify specific cell-surface markers opening avenues for differentiation refinement, NC purification, and functional studies. Altogether, this study provides a human notochord transcriptomic reference that will serve as a resource for notochord identification in human systems, diseased-tissues modeling, and facilitating future biomedical research.

Models of Congenital Adrenal Hyperplasia for Gene Therapies Testing

The adrenal glands are important endocrine organs that play a major role in the stress response. Some adrenal glands abnormalities are treated with hormone replacement therapy, which does not address physiological requirements. Modern technologies make it possible to develop gene therapy drugs that can completely cure diseases caused by mutations in specific genes. Congenital adrenal hyperplasia (CAH) is an example of such a potentially treatable monogenic disease. CAH is an autosomal recessive inherited disease with an overall incidence of 1:9500–1:20,000 newborns. To date, there are several promising drugs for CAH gene therapy. At the same time, it remains unclear how new approaches can be tested, as there are no models for this disease. The present review focuses on modern models for inherited adrenal gland insufficiency and their detailed characterization. In addition, the advantages and disadvantages of various pathological models are discussed, and ways of further development are suggested.

Computational analysis of synergism in small networks with different logic

Cell fate decision processes are regulated by networks which contain different molecules and interactions. Different network topologies may exhibit synergistic or antagonistic effects on cellular functions. Here, we analyze six most common small networks with regulatory logic AND or OR, trying to clarify the relationship between network topologies and synergism (or antagonism) related to cell fate decisions. We systematically examine the contribution of both network topologies and regulatory logic to the cell fate synergism by bifurcation and combinatorial perturbation analysis. Initially, under a single set of parameters, the synergism of three types of networks with AND and OR logic is compared. Furthermore, to consider whether these results depend on the choices of parameter values, statistics on the synergism of five hundred parameter sets is performed. It is shown that the results are not sensitive to parameter variations, indicating that the synergy or antagonism mainly depends on the network topologies rather than the choices of parameter values. The results indicate that the topology with "Dual Inhibition" shows good synergism, while the topology with "Dual Promotion" or "Hybrid" shows antagonism. The results presented here may help us to design synergistic networks based on network structure and regulation combinations, which has promising implications for cell fate decisions and drug combinations.

Understanding the interplay of membrane trafficking, cell surface mechanics, and stem cell differentiation

Stem cells can generate a diversity of cell types during development, regeneration and adult tissue homeostasis. Differentiation changes not only the cell fate in terms of gene expression but also the physical properties and functions of cells, e.g. the secretory activity, cell shape, or mechanics. Conversely, these activities and properties can also regulate differentiation itself. Membrane trafficking is known to modulate signal transduction and thus has the potential to control stem cell differentiation. On the other hand, membrane trafficking, particularly from and to the plasma membrane, depends on the mechanical properties of the cell surface such as tension within the plasma membrane or the cortex. Indeed, recent findings demonstrate that cell surface mechanics can also control cell fate. Here, we review the bidirectional relationships between these three fundamental cellular functions, i.e. membrane trafficking, cell surface mechanics, and stem cell differentiation. Furthermore, we discuss commonly used methods in each field and how combining them with new tools will enhance our understanding of their interplay. Understanding how membrane trafficking and cell surface mechanics can guide stem cell fate holds great potential as these concepts could be exploited for directed differentiation of stem cells for the fields of tissue engineering and regenerative medicine.

Puerarin action on stem cell proliferation, differentiation and apoptosis: Therapeutic implications for geriatric diseases

BACKGROUND

Aging is associated with a decline in cognitive and physical functions and various geriatric diseases, such as cardiovascular and neurodegenerative diseases. Puerarin (Pue), one of the main active flavonoids of Radix Puerariae (R. pueraria), is reportedly effective in treating geriatric diseases, including cardiovascular disease and hypertension.

PURPOSE

This review aims to summarize and discuss the profound physiological impact of Pue on various stem cell populations and provide new insights into the use of Pue for the prevention and treatment of geriatric diseases.

METHODS

The literature was retrieved from the core collection of electronic databases, such as Web of Science, Google Scholar, PubMed, and Science Direct, using the following keywords and terms: Puerarin, Stem Cell, Proliferation, Differentiation, Apoptosis, and Geriatric diseases. These keywords were used in multiple overlapping combinations.

RESULTS

Pue is effective in the treatment and management of age-related diseases, such as cardiovascular disease, diabetes, hypertension, and cerebrovascular disease. Pue exerts significant physiological effects on various stem cell populations, including their self-renewal/proliferation, differentiation and apoptosis. Most importantly, it could improve the efficiency and accuracy of stem cell therapy for treating various geriatric diseases. Further studies are essential to improve our understanding of the underlying mechanisms and elucidate their significance for future clinical applications.

CONCLUSION

The effects of Pue on various stem cell populations and their regulatory mechanisms are discussed in detail to provide new insights into the use of Pue in the prevention and treatment of geriatric diseases.

Recapitulating human myogenesis ex vivo using human pluripotent stem cells

Human pluripotent stem cells (hPSCs) provide a human model for developmental myogenesis, disease modeling and development of therapeutics. Differentiation of hPSCs into muscle stem cells has the potential to provide a cell-based therapy for many skeletal muscle wasting diseases. This review describes the current state of hPSCs towards recapitulating human myogenesis ex vivo, considerations of stem cell and progenitor cell state as well as function for future use of hPSC-derived muscle cells in regenerative medicine.

Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation

Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.

Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions

Existing computational methods that use single-cell RNA-sequencing (scRNA-seq) for cell fate prediction do not model how cells evolve stochastically and in physical time, nor can they predict how differentiation trajectories are altered by proposed interventions. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from time-series scRNA-seq data. We validate PRESCIENT on an experimental lineage tracing dataset, where we show that PRESCIENT is able to predict the fate biases of progenitor cells in hematopoiesis when accounting for cell proliferation, improving upon the best-performing existing method. We demonstrate how PRESCIENT can simulate trajectories for perturbed cells, recovering the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations, and is available at https://github.com/gifford-lab/prescient .

Using CRISPR to understand and manipulate gene regulation

Understanding how genes are expressed in the correct cell types and at the correct level is a key goal of developmental biology research. Gene regulation has traditionally been approached largely through observational methods, whereas perturbational approaches have lacked precision. CRISPR-Cas9 has begun to transform the study of gene regulation, allowing for precise manipulation of genomic sequences, epigenetic functionalization and gene expression. CRISPR-Cas9 technology has already led to the discovery of new paradigms in gene regulation and, as new CRISPR-based tools and methods continue to be developed, promises to transform our knowledge of the gene regulatory code and our ability to manipulate cell fate. Here, we discuss the current and future application of the emerging CRISPR toolbox toward predicting gene regulatory network behavior, improving stem cell disease modeling, dissecting the epigenetic code, reprogramming cell fate and treating diseases of gene dysregulation.