Automatic identification of small molecules that promote cell conversion and reprogramming

AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

BACKGROUND

Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course.

METHODS

We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data.

RESULTS

We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient's risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922).

CONCLUSIONS

The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient's risk scores can be used in clinical settings.

iPSC-derived cells for whole liver bioengineering

Liver bioengineering stands as a prominent alternative to conventional hepatic transplantation. Through liver decellularization and/or bioprinting, researchers can generate acellular scaffolds to overcome immune rejection, genetic manipulation, and ethical concerns that often accompany traditional transplantation methods, in vivo regeneration, and xenotransplantation. Hepatic cell lines derived from induced pluripotent stem cells (iPSCs) can repopulate decellularized and bioprinted scaffolds, producing an increasingly functional organ potentially suitable for autologous use. In this mini-review, we overview recent advancements in vitro hepatocyte differentiation protocols, shedding light on their pivotal role in liver recellularization and bioprinting, thereby offering a novel source for hepatic transplantation. Finally, we identify future directions for liver bioengineering research that may allow the implementation of these systems for diverse applications, including drug screening and liver disease modeling.

DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion

MOTIVATION

Direct reprogramming (DR) is a process that directly converts somatic cells to target cells. Although DR via small molecules is safer than using transcription factors (TFs) in terms of avoidance of tumorigenic risk, the determination of DR-inducing small molecules is challenging.

RESULTS

Here we present a novel in silico method, DIRECTEUR, to predict small molecules that replace TFs for DR. We extracted DR-characteristic genes using transcriptome profiles of cells in which DR was induced by TFs, and performed a variant of simulated annealing to explore small molecule combinations with similar gene expression patterns with DR-inducing TFs. We applied DIRECTEUR to predicting combinations of small molecules that convert fibroblasts into neurons or cardiomyocytes, and were able to reproduce experimentally verified and functionally related molecules inducing the corresponding conversions. The proposed method is expected to be useful for practical applications in regenerative medicine.

AVAILABILITY AND IMPLEMENTATION

The code and data are available at the following link: https://github.com/HamanoLaboratory/DIRECTEUR.git.

Chemical Transdifferentiation of Somatic Cells: Unleashing the Power of Small Molecules

Chemical transdifferentiation is a technique that utilizes small molecules to directly convert one cell type into another without passing through an intermediate stem cell state. This technique offers several advantages over other methods of cell reprogramming, such as simplicity, standardization, versatility, no ethical and safety concern and patient-specific therapies. Chemical transdifferentiation has been successfully applied to various cell types across different tissues and organs, and its potential applications are rapidly expanding as scientists continue to explore new combinations of small molecules and refine the mechanisms driving cell fate conversion. These applications have opened up new possibilities for regenerative medicine, disease modeling, drug discovery and tissue engineering. However, there are still challenges and limitations that need to be overcome before chemical transdifferentiation can be translated into clinical practice. These include low efficiency and reproducibility, incomplete understanding of the molecular mechanisms, long-term stability and functionality of the transdifferentiated cells, cell-type specificity and scalability. In this review, we compared the commonly used methods for cell transdifferentiation in recent years and discussed the current progress and future perspective of the chemical transdifferentiation of somatic cells and its potential impact on biomedicine. We believe that with ongoing research and technological advancements, the future holds tremendous promise for harnessing the power of small molecules to shape the cellular landscape and revolutionize the field of biomedicine.

PC3T: a signature-driven predictor of chemical compounds for cellular transition

Cellular transitions hold great promise in translational medicine research. However, therapeutic applications are limited by the low efficiency and safety concerns of using transcription factors. Small molecules provide a temporal and highly tunable approach to overcome these issues. Here, we present PC3T, a computational framework to enrich molecules that induce desired cellular transitions, and PC3T was able to consistently enrich small molecules that had been experimentally validated in both bulk and single-cell datasets. We then predicted small molecule reprogramming of fibroblasts into hepatic progenitor-like cells (HPLCs). The converted cells exhibited epithelial cell-like morphology and HPLC-like gene expression pattern. Hepatic functions were also observed, such as glycogen storage and lipid accumulation. Finally, we collected and manually curated a cell state transition resource containing 224 time-course gene expression datasets and 153 cell types. Our framework, together with the data resource, is freely available at http://pc3t.idrug.net.cn/ . We believe that PC3T is a powerful tool to promote chemical-induced cell state transitions.

Chemical reprogramming for cell fate manipulation: Methods, applications, and perspectives

Chemical reprogramming offers an unprecedented opportunity to control somatic cell fate and generate desired cell types including pluripotent stem cells for applications in biomedicine in a precise, flexible, and controllable manner. Recent success in the chemical reprogramming of human somatic cells by activating a regeneration-like program provides an alternative way of producing stem cells for clinical translation. Likewise, chemical manipulation enables the capture of multiple (stem) cell states, ranging from totipotency to the stabilization of somatic fates in vitro. Here, we review progress in using chemical approaches for cell fate manipulation in addition to future opportunities in this promising field.

Pluripotent Stem Cells of Order Carnivora: Technical Perspective

Human and mouse induced pluripotent stem cells (PSCs) are widely used for studying early embryonic development and for modeling of human diseases. Derivation and studying of PSCs from model organisms beyond commonly used mice and rats may provide new insights into the modeling and treating human diseases. The order Carnivora representatives possess unique features and are already used for modeling human-related traits. This review focuses on the technical aspects of derivation of the Carnivora species PSCs as well as their characterization. Current data on dog, feline, ferret, and American mink PSCs are summarized.

A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion

Cellular conversion can be induced by perturbing a handful of key transcription factors (TFs). Replacement of direct manipulation of key TFs with chemical compounds offers a less laborious and safer strategy to drive cellular conversion for regenerative medicine. Nevertheless, identifying optimal chemical compounds currently requires large-scale screening of chemical libraries, which is resource intensive. Existing computational methods aim at predicting cell conversion TFs, but there are no methods for identifying chemical compounds targeting these TFs. Here, we develop a single cell-based platform (SiPer) to systematically prioritize chemical compounds targeting desired TFs to guide cellular conversions. SiPer integrates a large compendium of chemical perturbations on non-cancer cells with a network model and predicted known and novel chemical compounds in diverse cell conversion examples. Importantly, we applied SiPer to develop a highly efficient protocol for human hepatic maturation. Overall, SiPer provides a valuable resource to efficiently identify chemical compounds for cell conversion.

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells, such as cells related to non-cancer disease, immunology and aging. Here, we present ChemPert (https://chempert.uni.lu/), a database consisting of 82 270 transcriptional signatures in response to 2566 unique perturbagens (drugs, small molecules and protein ligands) across 167 non-cancer cell types, as well as the protein targets of 57 818 perturbagens. In addition, we develop a computational tool that leverages the non-cancer cell datasets, which enables more accurate predictions of perturbation responses and drugs in non-cancer cells compared to those based onto cancer databases. In particular, ChemPert correctly predicted drug effects for treating hepatitis and novel drugs for osteoarthritis. The ChemPert web interface is user-friendly and allows easy access of the entire datasets and the computational tool, providing valuable resources for both experimental researchers who wish to find datasets relevant to their research and computational researchers who need comprehensive non-cancer perturbation transcriptomics datasets for developing novel algorithms. Overall, ChemPert will facilitate future in silico compound screening for non-cancer cells.

Microfluidics for Neuronal Cell and Circuit Engineering

The widespread adoption of microfluidic devices among the neuroscience and neurobiology communities has enabled addressing a broad range of questions at the molecular, cellular, circuit, and system levels. Here, we review biomedical engineering approaches that harness the power of microfluidics for bottom-up generation of neuronal cell types and for the assembly and analysis of neural circuits. Microfluidics-based approaches are instrumental to generate the knowledge necessary for the derivation of diverse neuronal cell types from human pluripotent stem cells, as they enable the isolation and subsequent examination of individual neurons of interest. Moreover, microfluidic devices allow to engineer neural circuits with specific orientations and directionality by providing control over neuronal cell polarity and permitting the isolation of axons in individual microchannels. Similarly, the use of microfluidic chips enables the construction not only of 2D but also of 3D brain, retinal, and peripheral nervous system model circuits. Such brain-on-a-chip and organoid-on-a-chip technologies are promising platforms for studying these organs as they closely recapitulate some aspects of in vivo biological processes. Microfluidic 3D neuronal models, together with 2D in vitro systems, are widely used in many applications ranging from drug development and toxicology studies to neurological disease modeling and personalized medicine. Altogether, microfluidics provide researchers with powerful systems that complement and partially replace animal models.

Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era

Recent advances in direct cell reprogramming have made possible the conversion of one cell type to another cell type, offering a potential cell-based treatment to many major diseases. Despite much attention, substantial roadblocks remain including the inefficiency in the proportion of reprogrammed cells of current experiments, and the requirement of a significant amount of time and resources. To this end, several computational algorithms have been developed with the goal of guiding the hypotheses to be experimentally validated. These approaches can be broadly categorized into two main types: transcription factor identification methods which aim to identify candidate transcription factors for a desired cell conversion, and transcription factor perturbation methods which aim to simulate the effect of a transcription factor perturbation on a cell state. The transcription factor perturbation methods can be broken down into Boolean networks, dynamical systems and regression models. We summarize the contributions and limitations of each method and discuss the innovation that single cell technologies are bringing to these approaches and we provide a perspective on the future direction of this field.

Determination of protoplast growth properties using quantitative single-cell tracking analysis

BACKGROUND

Although quantitative single-cell analysis is frequently applied in animal systems, e.g. to identify novel drugs, similar applications on plant single cells are largely missing. We have exploited the applicability of high-throughput microscopic image analysis on plant single cells using tobacco leaf protoplasts, cell-wall free single cells isolated by lytic digestion. Protoplasts regenerate their cell wall within several days after isolation and have the potential to expand and proliferate, generating microcalli and finally whole plants after the application of suitable regeneration conditions.

RESULTS

High-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts during the initial days following cultivation by immobilization in multi-well-plates. The focus on early protoplast responses allowed to study cell expansion prior to the initiation of proliferation and without the effects of shape-compromising cell walls. We compared growth parameters of wild-type tobacco cells with cells expressing the antiapoptotic protein Bcl2-associated athanogene 4 from Arabidopsis (AtBAG4).

CONCLUSIONS

AtBAG4-expressing protoplasts showed a higher proportion of cells responding with positive area increases than the wild type and showed increased growth rates as well as increased proliferation rates upon continued cultivation. These features are associated with reported observations on a BAG4-mediated increased resilience to various stress responses and improved cellular survival rates following transformation approaches. Moreover, our single-cell expansion results suggest a BAG4-mediated, cell-independent increase of potassium channel abundance which was hitherto reported for guard cells only. The possibility to explain plant phenotypes with single-cell properties, extracted with the single-cell processing and analysis pipeline developed, allows to envision novel biotechnological screening strategies able to determine improved plant properties via single-cell analysis.

Small molecules mediate cellular reprogramming across two kingdoms

The fertilized egg is the single totipotent cell from which multicellular organisms arise through the processes of cell division and differentiation. While animals typically lose their capacity to redifferentiate cells that are already fully differentiated, plant cells are thought to remain totipotent (Su et al., 2020). Every gardener knows well that plants can regenerate a full array of plant tissues from already differentiated organs. This also seems to be true for single plant cells such as protoplasts, which, under proper in vitro culture conditions, served as the initial source for generation of transgenic plants (Skoog and Miller, 1957; Birnbaum and Sánchez Alvarado, 2008). However, the mechanisms behind the totipotency of plant cells remain elusive, with the exception of the knowledge that the developmental fate of regenerating tissues can be directed by the ratio of two plant hormones, auxin and cytokinin (Skoog and Miller, 1957).