Computational Tools for Stem Cell Biology

The global evolution and impact of systems biology and artificial intelligence in stem cell research and therapeutics development: a scoping review

Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8 to 10-fold increase in research output related to all 3 search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the (US, n = 1487), (UK, n = 1094), Germany (n = 355), The Netherlands (n = 339), Russia (n = 215), and France (n = 149), while for AI-related research the US (n = 853) and UK (n = 258) take a strong lead, followed by Switzerland (n = 69), The Netherlands (n = 37), and Germany (n = 19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection among AI, SysBio, and SC research over the past 2 decades, with substantial growth in all 3 fields and exponential increases in AI-related research in the past decade.

Synergies in stem cell research: Integrating technologies, strategies, and bionanomaterial innovations

Since the 1960 s, there has been a substantial amount of research directed towards investigating the biology of several types of stem cells, including embryonic stem cells, brain cells, and mesenchymal stem cells. In contemporary times, a wide array of stem cells has been utilized to treat several disorders, including bone marrow transplantation. In recent years, stem cell treatment has developed as a very promising and advanced field of scientific research. The progress of therapeutic methodologies has resulted in significant amounts of anticipation and expectation. Recently, there has been a notable proliferation of experimental methodologies aimed at isolating and developing stem cells, which have emerged concurrently. Stem cells possess significant vitality and exhibit vigorous proliferation, making them suitable candidates for in vitro modification. This article examines the progress made in stem cell isolation and explores several methodologies employed to promote the differentiation of stem cells. This study also explores the method of isolating bio-nanomaterials and discusses their viewpoint in the context of stem cell research. It also covers the potential for investigating stem cell applications in bioprinting and the usage of bionanomaterial in stem cell-related technologies and research. In conclusion, the review article concludes by highlighting the importance of incorporating state-of-the-art methods and technological breakthroughs into the future of stem cell research. Putting such an emphasis on constant innovation highlights the ever-changing character of science and the never-ending drive toward unlocking the maximum therapeutic potential of stem cells. This review would be a useful resource for researchers, clinicians, and policymakers in the stem cell research area, guiding the next steps in this fast-developing scientific concern.

Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell-cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.

Direct reprogramming of somatic cells into induced hepatocytes: Cracking the Enigma code

There is an unmet need for functional primary human hepatocytes to support the pharmaceutical and (bio)medical demand. The unique discovery, a decade ago, that somatic cells can be drawn out of their apparent biological lockdown to reacquire a pluripotent state has revealed a completely new avenue of possibilities for generating surrogate human hepatocytes. Since then, the number of papers reporting the direct conversion of somatic cells into induced hepatocytes (iHeps) has burgeoned. A hepatic cell fate can be established via the ectopic expression of native liver-enriched transcription factors in somatic cells, thereby bypassing the need for an intermediate (pluripotent) stem cell state. That said, understanding and eventually controlling the processes that give rise to functional iHeps remains challenging. In this review, we provide an overview of the state-of-the-art reprogramming cocktails and techniques, as well as their corresponding conversion efficiencies. Special attention is paid to the role of liver-enriched transcription factors as hepatogenic reprogramming tools and small molecules as facilitators of hepatic transdifferentiation. To conclude, we formulate recommendations to optimise, standardise and enrich the in vitro production of iHeps to reach clinical standards, and propose minimal criteria for their characterisation.

Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics

Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research.

Informatics Approaches for Harmonized Intelligent Integration of Stem Cell Research

As biomedical data integration and analytics play an increasing role in the field of stem cell research, it becomes important to develop ways to standardize, aggregate, and share data among researchers. For this reason, many databases have been developed in recent years in an attempt to systematically warehouse data from different stem cell projects and experiments at the same time. However, these databases vary widely in their implementation and structure. The aim of this scoping review is to characterize the main features of available stem cell databases in order to identify specifications useful for implementation in future stem cell databases. We conducted a scoping review of peer-reviewed literature and online resources to identify and review available stem cell databases. To identify the relevant databases, we performed a PubMed search using relevant MeSH terms followed by a web search for databases which may not have an associated journal article. In total, we identified 16 databases to include in this review. The data elements reported in these databases represented a broad spectrum of parameters from basic socio-demographic variables to various cells characteristics, cell surface markers expression, and clinical trial results. Three broad sets of functional features that provide utility for future stem cell research and facilitate bioinformatics workflows were identified. These features consisted of the following: common data elements, data visualization and analysis tools, and biomedical ontologies for data integration. Stem cell bioinformatics is a quickly evolving field that generates a growing number of heterogeneous data sets. Further progress in the stem cell research may be greatly facilitated by development of applications for intelligent stem cell data aggregation, sharing and collaboration process.

Computational Strategies for Niche-Dependent Cell Conversion to Assist Stem Cell Therapy

The field of regenerative medicine has blossomed in recent decades. However, the ultimate goal of tissue regeneration - replacing damaged or aged cells with healthy functioning cells - still faces a number of challenges. In particular, better understanding of the role of the cellular niche in shaping stem cell phenotype and conversion would aid in improving current protocols for stem cell therapies. In this regard, the implementation of novel computational approaches that consider the niche effect on stem cells would be valuable. Here we discuss current problems in stem cell transplantation and rejuvenation, and we propose computational strategies to control niche-dependent cell conversion to overcome them.

DeepNEU: cellular reprogramming comes of age - a machine learning platform with application to rare diseases research

BACKGROUND

Conversion of human somatic cells into induced pluripotent stem cells (iPSCs) is often an inefficient, time consuming and expensive process. Also, the tendency of iPSCs to revert to their original somatic cell type over time continues to be problematic. A computational model of iPSCs identifying genes/molecules necessary for iPSC generation and maintenance could represent a crucial step forward for improved stem cell research. The combination of substantial genetic relationship data, advanced computing hardware and powerful nonlinear modeling software could make the possibility of artificially-induced pluripotent stem cells (aiPSC) a reality. We have developed an unsupervised deep machine learning technology, called DeepNEU that is based on a fully-connected recurrent neural network architecture with one network processing layer for each input. DeepNEU was used to simulate aiPSC systems using a defined set of reprogramming transcription factors. Genes/proteins that were reported to be essential in human pluripotent stem cells (hPSC) were used for system modelling.

RESULTS

The Mean Squared Error (MSE) function was used to assess system learning. System convergence was defined at MSE < 0.001. The markers of human iPSC pluripotency (N = 15) were all upregulated in the aiPSC final model. These upregulated/expressed genes in the aiPSC system were entirely consistent with results obtained for iPSCs.

CONCLUSION

This research introduces and validates the potential use of aiPSCs as computer models of human pluripotent stem cell systems. Disease-specific aiPSCs have the potential to improve disease modeling, prototyping of wet lab experiments, and prediction of genes relevant and necessary for aiPSC production and maintenance for both common and rare diseases in a cost-effective manner.

Metabolomics in Stem Cell Biology Research

Stem cell research has been greatly facilitated by comprehensive and integrative multi-omics studies. As a unique approach of functional analysis, metabolomics measures many metabolites and activities of metabolic pathways which can directly indicate cellular energetic status, cell proliferation and fitness, and stem cell fate choices such as self-renewal versus differentiation. Here we describe the methods of applying metabolomics, ¹³C-labeled glucose and glutamine tracing with mouse embryonic stem cells (ES cells), metabolite analysis using mass spectrometry tools, and the following statistical and computational modeling analysis. Integration of these methods into the more common gene expression and epigenetics analysis toolbox will help to generate a more complete picture and in-depth understanding of one's stem cells of interest.

SeesawPred: A Web Application for Predicting Cell-fate Determinants in Cell Differentiation

Cellular differentiation is a complex process where a less specialized cell evolves into a more specialized cell. Despite the increasing research effort, identification of cell-fate determinants (transcription factors (TFs) determining cell fates during differentiation) still remains a challenge, especially when closely related cell types from a common progenitor are considered. Here, we develop SeesawPred, a web application that, based on a gene regulatory network (GRN) model of cell differentiation, can computationally predict cell-fate determinants from transcriptomics data. Unlike previous approaches, it allows the user to upload gene expression data and does not rely on pre-compiled reference data sets, enabling its application to novel differentiation systems. SeesawPred correctly predicted known cell-fate determinants on various cell differentiation examples in both mouse and human, and also performed better compared to state-of-the-art methods. The application is freely available for academic, non-profit use at http://seesaw.lcsb.uni.lu.

Temporal expression of pluripotency-associated transcription factors in sheep and cattle preimplantation embryos

SummaryPluripotency-associated transcription factors (PATFs) modulate gene expression during early mammalian embryogenesis. Despite a strong understanding of PATFs during mouse embryogenesis, limited progress has been made in ruminants. This work aimed to describe the temporal expression of eight PATFs during both sheep and cattle preimplantation development. Transcript availability of PATFs was evaluated by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) in eggs, cleavage-stage embryos, morulae, and blastocysts. Transcripts of five genes were detected in all developmental stages of both species (KLF5, OCT4, RONIN, ZFP281, and ZFX). Furthermore, CMYC was detected in all cattle samples but was found from cleavage-stage onwards in sheep. In contrast, NR0B1 was detected in all sheep samples but was not detected in cattle morulae. GLIS1 displayed the most significant variation in temporal expression between species, as this PATF was only detected in cattle eggs and sheep cleavage-stage embryos and blastocysts. In silico analysis suggested that cattle and sheep PATFs share similar size, isometric point and molecular weight. A phenetic analysis showed two patterns of PATF clustering between cattle and sheep, among several mammalian species. In conclusion, the temporal expression of pluripotency-associated transcription factors differs between sheep and cattle, suggesting species-specific regulation during preimplantation development.

Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics

Single-cell genomics has recently emerged as a powerful tool for observing multicellular systems at a much higher level of resolution and depth than previously possible. High-throughput single-cell RNA sequencing techniques are able to simultaneously quantify expression levels of several thousands of genes within individual cells for tens of thousands of cells within a complex tissue. This has led to development of novel computational methods to analyze this high-dimensional data, investigating longstanding and fundamental questions regarding the granularity of cell types, the definition of cell states, and transitions from one cell type to another along developmental trajectories. In this perspective, we outline this emerging field starting from the "input data" (e.g., quantifying transcription levels in single cells), which are analyzed to define "identities" (e.g., cell types, states, and key genes) and to build "interactions" using models that can infer relations and transitions between cells.

Induced pluripotent stem cells derived from human amnion in chemically defined conditions

Fetal stem cells are a unique type of adult stem cells that have been suggested to be broadly multipotent with some features of pluripotency. Their clinical potential has been documented but their upgrade to full pluripotency could open up a wide range of cell-based therapies particularly suited for pediatric tissue engineering, longitudinal studies or disease modeling. Here we describe episomal reprogramming of mesenchymal stem cells from the human amnion to pluripotency (AM-iPSC) in chemically defined conditions. The AM-iPSC expressed markers of embryonic stem cells, readily formed teratomas with tissues of all three germ layers present and had a normal karyotype after around 40 passages in culture. We employed novel computational methods to determine the degree of pluripotency from microarray and RNA sequencing data in these novel lines alongside an iPSC and ESC control and found that all lines were deemed pluripotent, however, with variable scores. Differential expression analysis then identified several groups of genes that potentially regulate this variability in lines within the boundaries of pluripotency, including metallothionein proteins. By further studying this variability, characteristics relevant to cell-based therapies, like differentiation propensity, could be uncovered and predicted in the pluripotent stage.

A stochastic and dynamical view of pluripotency in mouse embryonic stem cells

Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals.

Endothelial stem and progenitor cells (stem cells): (2017 Grover Conference Series)

The capacity of existing blood vessels to give rise to new blood vessels via endothelial cell sprouting is called angiogenesis and is a well-studied biologic process. In contrast, little is known about the mechanisms for endothelial cell replacement or regeneration within established blood vessels. Since clear definitions exist for identifying cells with stem and progenitor cell properties in many tissues and organs of the body, several groups have begun to accumulate evidence that endothelial stem and progenitor cells exist within the endothelial intima of existing blood vessels. This paper will review stem and progenitor cell definitions and highlight several recent papers purporting to have identified resident vascular endothelial stem and progenitor cells.

Genomic and molecular control of cell type and cell type conversions

Organisms are made of a limited number of cell types that combine to form higher order tissues and organs. Cell types have traditionally been defined by their morphologies or biological activity, yet the underlying molecular controls of cell type remain unclear. The onset of single cell technologies, and more recently genomics (particularly single cell genomics), has substantially increased the understanding of the concept of cell type, but has also increased the complexity of this understanding. These new technologies have added a new genome wide molecular dimension to the description of cell type, with genome-wide expression and epigenetic data acting as a cell type ‘fingerprint’ to describe the cell state. Using these genomic fingerprints cell types are being increasingly defined based on specific genomic and molecular criteria, without necessarily a distinct biological function. In this review, we will discuss the molecular definitions of cell types and cell type control, and particularly how endogenous and exogenous transcription factors can control cell types and cell type conversions.

Functional and Biomimetic Materials for Engineering of the Three-Dimensional Cell Microenvironment

The cell microenvironment has emerged as a key determinant of cell behavior and function in development, physiology, and pathophysiology. The extracellular matrix (ECM) within the cell microenvironment serves not only as a structural foundation for cells but also as a source of three-dimensional (3D) biochemical and biophysical cues that trigger and regulate cell behaviors. Increasing evidence suggests that the 3D character of the microenvironment is required for development of many critical cell responses observed in vivo, fueling a surge in the development of functional and biomimetic materials for engineering the 3D cell microenvironment. Progress in the design of such materials has improved control of cell behaviors in 3D and advanced the fields of tissue regeneration, in vitro tissue models, large-scale cell differentiation, immunotherapy, and gene therapy. However, the field is still in its infancy, and discoveries about the nature of cell-microenvironment interactions continue to overturn much early progress in the field. Key challenges continue to be dissecting the roles of chemistry, structure, mechanics, and electrophysiology in the cell microenvironment, and understanding and harnessing the roles of periodicity and drift in these factors. This review encapsulates where recent advances appear to leave the ever-shifting state of the art, and it highlights areas in which substantial potential and uncertainty remain.

Understanding development and stem cells using single cell-based analyses of gene expression

In recent years, genome-wide profiling approaches have begun to uncover the molecular programs that drive developmental processes. In particular, technical advances that enable genome-wide profiling of thousands of individual cells have provided the tantalizing prospect of cataloging cell type diversity and developmental dynamics in a quantitative and comprehensive manner. Here, we review how single-cell RNA sequencing has provided key insights into mammalian developmental and stem cell biology, emphasizing the analytical approaches that are specific to studying gene expression in single cells.

Engineering kidney cells: reprogramming and directed differentiation to renal tissues

Growing knowledge of how cell identity is determined at the molecular level has enabled the generation of diverse tissue types, including renal cells from pluripotent or somatic cells. Recently, several in vitro protocols involving either directed differentiation or transcription-factor-based reprogramming to kidney cells have been established. Embryonic stem cells or induced pluripotent stem cells can be guided towards a kidney fate by exposing them to combinations of growth factors or small molecules. Here, renal development is recapitulated in vitro resulting in kidney cells or organoids that show striking similarities to mammalian embryonic nephrons. In addition, culture conditions are also defined that allow the expansion of renal progenitor cells in vitro. Another route towards the generation of kidney cells is direct reprogramming. Key transcription factors are used to directly impose renal cell identity on somatic cells, thus circumventing the pluripotent stage. This complementary approach to stem-cell-based differentiation has been demonstrated to generate renal tubule cells and nephron progenitors. In-vitro-generated renal cells offer new opportunities for modelling inherited and acquired renal diseases on a patient-specific genetic background. These cells represent a potential source for developing novel models for kidney diseases, drug screening and nephrotoxicity testing and might represent the first steps towards kidney cell replacement therapies. In this review, we summarize current approaches for the generation of renal cells in vitro and discuss the advantages of each approach and their potential applications.

Assessment of engineered cells using CellNet and RNA-seq

CellNet is a computational platform designed to assess cell populations engineered by either directed differentiation of pluripotent stem cells (PSCs) or direct conversion, and to suggest specific hypotheses to improve cell fate engineering protocols. CellNet takes as input gene expression data and compares them with large data sets of normal expression profiles compiled from public sources, in regard to the extent to which cell- and tissue-specific gene regulatory networks are established. CellNet was originally designed to work with human or mouse microarray expression data for 21 cell or tissue (C/T) types. Here we describe how to apply CellNet to RNA-seq data and how to build a completely new CellNet platform applicable to, for example, other species or additional cell and tissue types. Once the raw data have been preprocessed, running CellNet takes only several minutes, whereas the time required to create a completely new CellNet is several hours.

A comparative review of computational methods for pathway perturbation analysis: dynamical and topological perspectives

Stem cells offer great promise within the field of regenerative medicine but despite encouraging results, the large scale use of stem cells for therapeutic applications still faces challenges when it comes to controlling signaling pathway responses with respect to environmental perturbations.