Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes

Single-cell transcriptomics reveals liver developmental trajectory during lineage reprogramming of human induced hepatocyte-like cells

Hepatocytes are crucial for drug screening, disease modeling, and clinical transplantation, yet generating functional hepatocytes in vitro is challenging due to the difficulty of establishing their authentic gene regulatory networks (GRNs). We have previously developed a two-step lineage reprogramming strategy to generate functionally competent human induced hepatocytes (hiHeps), providing an effective model for studying the establishment of hepatocyte-specific GRNs. In this study, we utilized high-throughput single-cell RNA sequencing (scRNA-seq) to explore the cell-fate transition and the establishment of hepatocyte-specific GRNs involved in the two-step reprogramming process. Our findings revealed that the late stage of the reprogramming process mimics the natural trajectory of liver development, exhibiting similar transcriptional waves of developmental genes. CD24 and DLK1 were identified as surface markers enriching two distinct hepatic progenitor populations respectively. Lipid metabolism emerged as a key enhancer of hiHeps maturation. Furthermore, transcription factors HNF4A and HHEX were identified as pivotal gatekeepers directing cell fate decisions between hepatocytes and intestinal cells. Collectively, this study provides valuable insights into the establishment of hepatocyte-specific GRNs during hiHeps induction at single-cell resolution, facilitating more efficient production of functional hepatocytes for therapeutic applications.

Pre-existing stem cell heterogeneity dictates clonal responses to the acquisition of leukemic driver mutations

Cancer cells display wide phenotypic variation even across patients with the same mutations. Differences in the cell of origin provide a potential explanation, but traditional assays lack the resolution to distinguish clonally heterogeneous subsets of stem and progenitor cells. To address this challenge, we developed simultaneous tracking of recombinase activation and clonal kinetics (STRACK), a method to trace clonal dynamics and gene expression before and after the acquisition of cancer mutations. Using mouse models, we studied two leukemic mutations, Dnmt3a-R878H and Npm1c, and found that their effect was highly variable across different stem cell states. Specifically, a subset of differentiation-primed stem cells, which normally becomes outcompeted with time, expands with both mutations. Intriguingly, Npm1c mutations reversed the intrinsic bias of the clone of origin, with differentiation-primed stem cells giving rise to more primitive malignant states. Thus, we highlight the relevance of single-cell lineage tracing to unravel early events in cancer evolution and posit that different cellular histories carry distinct cancer phenotypic potential.

Advances in humanoid organoid-based research on inter-organ communications during cardiac organogenesis and cardiovascular diseases

The intimate correlation between cardiovascular diseases and other organ pathologies, such as metabolic and kidney diseases, underscores the intricate interactions among these organs. Understanding inter-organ communications is crucial for developing more precise drugs and effective treatments for systemic diseases. While animal models have traditionally been pivotal in studying these interactions, human-induced pluripotent stem cells (hiPSCs) offer distinct advantages when constructing in vitro models. Beyond the conventional two-dimensional co-culture model, hiPSC-derived humanoid organoids have emerged as a substantial advancement, capable of replicating essential structural and functional attributes of internal organs in vitro. This breakthrough has spurred the development of multilineage organoids, assembloids, and organoids-on-a-chip technologies, which allow for enhanced physiological relevance. These technologies have shown great potential for mimicking coordinated organogenesis, exploring disease pathogenesis, and facilitating drug discovery. As the central organ of the cardiovascular system, the heart serves as the focal point of an extensively studied network of interactions. This review focuses on the advancements and challenges of hiPSC-derived humanoid organoids in studying interactions between the heart and other organs, presenting a comprehensive exploration of this cutting-edge approach in systemic disease research.

Cell Function Graphics: TOGGLE delineates fate and function within individual cell types via single-cell transcriptomics

Functional RNA plays a crucial role in regulating cellular processes throughout the life cycle of a cell. Identifying functional changes at each stage, from inception to development to maturation, functional execution, and eventual death or pathological transformation, often requires systematic comparisons of functional expression across cell populations. However, because cells of the same type often exhibit similar gene expression patterns regardless of function or fate, it is challenging to distinguish the stages of cellular fate or functional states within the same cell type, which also limits our understanding of cellular memory. Cells of the same type that share structural and gene expression similarities but originate from different regions and perform slightly distinct functions often retain unique epigenetic memory signatures. Although RNA serves as a key executor of fundamental cellular functions, its high expression similarity among cells of the same type limits its ability to distinguish functional heterogeneity. To overcome this challenge, we developed TOGGLE, utilizing higher-resolution analytical methods to uncover functional diversity at the cellular level. Then we based on TOGGLE developed an innovative Graph Diffusion Functional Map, which can significantly reduce noise, thereby more clearly displaying the functional grouping of RNA and enabling the capture of more subtle functional differences in high-dimensional data. Ultimately, this method effectively removes the influence of baseline functions from classification criteria and identifies key trajectories of cell fate determination.

Proliferation history and transcription factor levels drive direct conversion to motor neurons

The sparse and stochastic nature of conversion has obscured our understanding of how transcription factors (TFs) drive cells to new identities. To overcome this limit, we develop a tailored, high-efficiency conversion system that increases the direct conversion of fibroblasts to motor neurons 100-fold. By tailoring the cocktail to a minimal set of transcripts, we reduce extrinsic variation, allowing us to examine how proliferation and TFs synergistically drive conversion. We show that cell state-as set by proliferation history-defines how cells interpret the levels of TFs. Controlling for proliferation history and titrating each TF, we find that conversion correlates with levels of the pioneer TF Ngn2. By isolating cells by both their proliferation history and Ngn2 levels, we demonstrate that levels of Ngn2 expression alone are insufficient to predict conversion rates. Rather, proliferation history and TF levels combine to drive direct conversion. Finally, increasing the proliferation rate of adult human fibroblasts generates morphologically mature induced human motor neurons at high rates.

Generative AI Models in Time-Varying Biomedical Data: Scoping Review

BACKGROUND

Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care.

OBJECTIVE

While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science.

METHODS

We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality.

RESULTS

We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand.

CONCLUSIONS

We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.

High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations

In vivo lineage tracing holds great potential to reveal fundamental principles of tissue development and homeostasis. However, current lineage tracing in humans relies on extremely rare somatic mutations, which has limited temporal resolution and lineage accuracy. Here, we developed a generic lineage-tracing tool based on frequent epimutations on DNA methylation, enabled by our computational method MethylTree. Using single-cell genome-wide DNA methylation datasets with known lineage and phenotypic labels, MethylTree reconstructed lineage histories at nearly 100% accuracy across different cell types, developmental stages, and species. We demonstrated the epimutation-based single-cell multi-omic lineage tracing in mouse and human blood, where MethylTree recapitulated the differentiation hierarchy in hematopoiesis. Applying MethylTree to human embryos, we revealed early fate commitment at the four-cell stage. In native mouse blood, we identified ~250 clones of hematopoietic stem cells. MethylTree opens the door for high-resolution, noninvasive and multi-omic lineage tracing in humans and beyond.

Interplay between pioneer transcription factors and epigenetic modifiers in cell reprogramming

The generation of induced pluripotent stem cells (iPSCs) from differentiated somatic cells by Yamanaka factors, including pioneer transcription factors (TFs), has greatly reshaped our traditional understanding of cell plasticity and demonstrated the remarkable potential of pioneer TFs. In addition to iPSC reprogramming, pioneer TFs are pivotal in direct reprogramming or transdifferentiation where somatic cells are converted into different cell types without passing through a pluripotent state. Pioneer TFs initiate a reprogramming process through chromatin opening, thereby establishing competence for new gene regulatory programs. The action of pioneer TFs is both influenced by and exerts influence on epigenetic regulation. Despite significant advances, many direct reprogramming processes remain inefficient, which limits their reliability for clinical applications. In this review, we discuss the molecular mechanisms underlying pioneer TF-driven reprogramming, with a focus on their interactions with epigenetic modifiers, including Polycomb repressive complexes (PRCs), nucleosome remodeling and deacetylase (NuRD) complexes, and the DNA methylation machinery. A deeper understanding of the dynamic interplay between pioneer TFs and epigenetic modifiers will be essential for advancing reprogramming technologies and unlocking their full clinical potential.

The landscape of cell lineage tracing

Cell fate changes play a crucial role in the processes of natural development, disease progression, and the efficacy of therapeutic interventions. The definition of the various types of cell fate changes, including cell expansion, differentiation, transdifferentiation, dedifferentiation, reprogramming, and state transitions, represents a complex and evolving field of research known as cell lineage tracing. This review will systematically introduce the research history and progress in this field, which can be broadly divided into two parts: prospective tracing and retrospective tracing. The initial section encompasses an array of methodologies pertaining to isotope labeling, transient fluorescent tracers, non-fluorescent transient tracers, non-fluorescent genetic markers, fluorescent protein, genetic marker delivery, genetic recombination, exogenous DNA barcodes, CRISPR-Cas9 mediated DNA barcodes, and base editor-mediated DNA barcodes. The second part of the review covers genetic mosaicism, genomic DNA alteration, TCR/BCR, DNA methylation, and mitochondrial DNA mutation. In the final section, we will address the principal challenges and prospective avenues of enquiry in the field of cell lineage tracing, with a particular focus on the sequencing techniques and mathematical models pertinent to single-cell genetic lineage tracing, and the value of pursuing a more comprehensive investigation at both the spatial and temporal levels in the study of cell lineage tracing.

Direct fibroblast reprogramming: an emerging strategy for treating organic fibrosis

Direct reprogramming has garnered considerable attention due to its capacity to directly convert differentiated cells into desired cells. Fibroblasts are frequently employed in reprogramming studies due to their abundance and accessibility. However, they are also the key drivers in the progression of fibrosis, a pathological condition characterized by excessive extracellular matrix deposition and tissue scarring. Furthermore, the initial stage of reprogramming typically involves deactivating fibrotic pathways. Hence, direct reprogramming offers a valuable method to regenerate target cells for tissue repair while simultaneously reducing fibrotic tendencies. Understanding the link between reprogramming and fibrosis could help develop effective strategies to treat damaged tissue with a potential risk of fibrosis. This review summarizes the advances in direct reprogramming and reveals their anti-fibrosis effects in various organs such as the heart, liver, and skin. Furthermore, we dissect the mechanisms of reprogramming influenced by fibrotic molecules including TGF-β signaling, mechanical signaling, inflammation signaling, epigenetic modifiers, and metabolic regulators. Innovative methods for fibroblast reprogramming like small molecules, CRISPRa, modified mRNA, and the challenges of cellular heterogeneity and senescence faced by in vivo direct reprogramming, are also discussed.

Mechanisms and environmental factors shaping the ecosystem of brain macrophages

Brain macrophages encompass two major populations: microglia in the parenchyma and border-associated macrophages (BAMs) in the extra-parenchymal compartments. These cells play crucial roles in maintaining brain homeostasis and immune surveillance. Microglia and BAMs are phenotypically and epigenetically distinct and exhibit highly specialized functions tailored to their environmental niches. Intriguingly, recent studies have shown that both microglia and BAMs originate from the same myeloid progenitor during yolk sac hematopoiesis, but their developmental fates diverge within the brain. Several works have partially unveiled the mechanisms orchestrating the development of microglia and BAMs in both mice and humans; however, many questions remain unanswered. Defining the molecular underpinnings controlling the transcriptional and epigenetic programs of microglia and BAMs is one of the upcoming challenges for the field. In this review, we outline current knowledge on ontogeny, phenotypic diversity, and the factors shaping the ecosystem of brain macrophages. We discuss insights garnered from human studies, highlighting similarities and differences compared to mice. Lastly, we address current research gaps and potential future directions in the field. Understanding how brain macrophages communicate with their local environment and how the tissue instructs their developmental trajectories and functional features is essential to fully comprehend brain physiology in homeostasis and disease.

Modeling non-genetic adaptation in tumor cells

Treatment resistance poses a significant challenge in the care of cancer patients. Hirsch et al. applied computational and genomic approaches, examining gene expression dynamics from a mouse model of melanoma at single-cell resolution to reveal that semi-heritable non-genetic alterations in tumor cell populations confer adaptive resistance to treatment.

scLTdb: a comprehensive single-cell lineage tracing database

Single-cell lineage tracing (scLT) is a powerful technique that integrates cellular barcoding with single-cell sequencing technologies. This new approach enables the simultaneous measurement of cell fate and molecular profiles at single-cell resolution, uncovering the gene regulatory program of cell fate determination. However, a comprehensive scLT database is not yet available. Here, we present the single-cell lineage tracing database (scLTdb, https://scltdb.com) containing 109 datasets that are manually curated and analyzed through a standard pipeline. The scLTdb provides interactive analysis modules for visualizing and re-analyzing scLT datasets, especially the comprehensive cell fate analysis and lineage relationship analysis. Importantly, scLTdb also allows users to identify fate-related gene signatures. In conclusion, scLTdb provides an interactive interface of scLT data exploration and analysis, and will facilitate the understanding of cell fate decision and lineage commitment in development and diseases.

Advancements in prospective single-cell lineage barcoding and their applications in research

Single-cell lineage tracing (scLT) has emerged as a powerful tool, providing unparalleled resolution to investigate cellular dynamics, fate determination, and the underlying molecular mechanisms. This review thoroughly examines the latest prospective lineage DNA barcode tracing technologies. It further highlights pivotal studies that leverage single-cell lentiviral integration barcoding technology to unravel the dynamic nature of cell lineages in both developmental biology and cancer research. Additionally, the review navigates through critical considerations for successful experimental design in lineage tracing and addresses challenges inherent in this field, including technical limitations, complexities in data analysis, and the imperative for standardization. It also outlines current gaps in knowledge and suggests future research directions, contributing to the ongoing advancement of scLT studies.

Applying single-cell and single-nucleus genomics to studies of cellular heterogeneity and cell fate transitions in the nervous system

Single-cell and single-nucleus genomic approaches can provide unbiased and multimodal insights. Here, we discuss what constitutes a molecular cell atlas and how to leverage single-cell omics data to generate hypotheses and gain insights into cell transitions in development and disease of the nervous system. We share points of reflection on what to consider during study design and implementation as well as limitations and pitfalls.

Epigenetic Dynamics in Reprogramming to Dopaminergic Neurons for Parkinson's Disease

Direct lineage reprogramming into dopaminergic (DA) neurons holds great promise for the more effective production of DA neurons, offering potential therapeutic benefits for conditions such as Parkinson's disease. However, the reprogramming pathway for fully reprogrammed DA neurons remains largely unclear, resulting in immature and dead-end states with low efficiency. In this study, using single-cell RNA sequencing, the trajectory of reprogramming DA neurons at multiple time points, identifying a continuous pathway for their reprogramming is analyzed. It is identified that intermediate cell populations are crucial for resetting host cell fate during early DA neuronal reprogramming. Further, longitudinal dissection uncovered two distinct trajectories: one leading to successful reprogramming and the other to a dead end. Notably, Arid4b, a histone modifier, as a crucial regulator at this branch point, essential for the successful trajectory and acquisition of mature dopaminergic neuronal identity is identified. Consistently, overexpressing Arid4b in the DA neuronal reprogramming process increases the yield of iDA neurons and effectively reverses the disease phenotypes observed in the PD mouse brain. Thus, gaining insights into the cellular trajectory holds significant importance for devising regenerative medicine strategies, particularly in the context of addressing neurodegenerative disorders like Parkinson's disease.

Inferring cell differentiation maps from lineage tracing data

During development, mulitpotent cells differentiate through a hierarchy of increasingly restricted progenitor cell types until they realize specialized cell types. A cell differentiation map describes this hierarchy, and inferring these maps is an active area of research spanning traditional single marker lineage studies to data-driven trajectory inference methods on single-cell RNA-seq data. Recent high-throughput lineage tracing technologies profile lineages and cell types at scale, but current methods to infer cell differentiation maps from these data rely on simple models with restrictive assumptions about the developmental process. We introduce a mathematical framework for cell differentiation maps based on the concept of potency, and develop an algorithm, Carta, that infers an optimal cell differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the trade-off between the complexity of the cell differentiation map and the number of unobserved cell type transitions on the lineage tree. We show that Carta more accurately infers cell differentiation maps on both simulated and real data compared to existing methods. In models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods including convergent differentiation of specialized cell types, progenitor differentiation dynamics, and the refinement of routes of differentiation via new intermediate progenitors.

New tools to study renal fibrogenesis

PURPOSE OF REVIEW

Kidney fibrosis is a key pathological aspect and outcome of chronic kidney disease (CKD). The advent of multiomic analyses using human kidney tissue, enabled by technological advances, marks a new chapter of discovery in fibrosis research of the kidney. This review highlights the rapid advancements of single-cell and spatial multiomic techniques that offer new avenues for exploring research questions related to human kidney fibrosis development.

RECENT FINDINGS

We recently focused on understanding the origin and transition of myofibroblasts in kidney fibrosis using single-cell RNA sequencing (scRNA-seq) [1] . We analysed cells from healthy human kidneys and compared them to patient samples with CKD. We identified PDGFRα+/PDGFRβ+ mesenchymal cells as the primary cellular source of extracellular matrix (ECM) in human kidney fibrosis. We found several commonly shared cell states of fibroblasts and myofibroblasts and provided insights into molecular regulators. Novel single-cell and spatial multiomics tools are now available to shed light on cell lineages, the plasticity of kidney cells and cell-cell communication in fibrosis.

SUMMARY

As further single-cell and spatial multiomic approaches are being developed, opportunities to apply these methods to human kidney tissues expand similarly. Careful design and optimisation of the multiomic experiments are needed to answer questions related to cell lineages, plasticity and cell-cell communication in kidney fibrosis.

Genetic Tools for Cell Lineage Tracing and Profiling Developmental Trajectories in the Skin

The epidermis is the body's first line of protection against dehydration and pathogens, continually regenerating the outermost protective skin layers throughout life. During both embryonic development and wound healing, epidermal stem and progenitor cells must respond to external stimuli and insults to build, maintain, and repair the cutaneous barrier. Recent advances in CRISPR-based methods for cell lineage tracing have remarkably expanded the potential for experiments that track stem and progenitor cell proliferation and differentiation over the course of tissue and even organismal development. Additional tools for DNA-based recording of cellular signaling cues promise to deepen our understanding of the mechanisms driving normal skin morphogenesis and response to stressors as well as the dysregulation of cell proliferation and differentiation in skin diseases and cancer. In this review, we highlight cutting-edge methods for cell lineage tracing, including in organoids and model organisms, and explore how cutaneous biology researchers might leverage these techniques to elucidate the developmental programs that support the regenerative capacity and plasticity of the skin.

Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets

Assigning single cell transcriptomes to cellular lineage trees by lineage tracing has transformed our understanding of differentiation during development, regeneration, and disease. However, lineage tracing is technically demanding, often restricted in time-resolution, and most scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational tool allowing to robustly identify small to medium-sized cell lineages solely from scRNA-seq datasets. GEMLI allows to study heritable gene expression, to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. In human breast cancer biopsies, GEMLI reveals previously unknown gene expression changes at the onset of cancer invasiveness. The universal applicability of GEMLI allows studying the role of small cell lineages in a wide range of physiological and pathological contexts, notably in vivo. GEMLI is available as an R package on GitHub ( https://github.com/UPSUTER/GEMLI ).

Extracting, filtering and simulating cellular barcodes using CellBarcode tools

Identifying true DNA cellular barcodes among polymerase chain reaction and sequencing errors is challenging. Current tools are restricted in the diversity of barcode types supported or the analysis strategies implemented. As such, there is a need for more versatile and efficient tools for barcode extraction, as well as for tools to investigate which factors impact barcode detection and which filtering strategies to best apply. Here we introduce the package CellBarcode and its barcode simulation kit, CellBarcodeSim, that allows efficient and versatile barcode extraction and filtering for a range of barcode types from bulk or single-cell sequencing data using a variety of filtering strategies. Using the barcode simulation kit and biological data, we explore the technical and biological factors influencing barcode identification and provide a decision tree on how to optimize barcode identification for different barcode settings. We believe that CellBarcode and CellBarcodeSim have the capability to enhance the reproducibility and interpretation of barcode results across studies.

Programming human cell fate: overcoming challenges and unlocking potential through technological breakthroughs

In recent years, there have been notable advancements in the ability to programme human cell identity, enabling us to design and manipulate cell function in a Petri dish. However, current protocols for generating target cell types often lack efficiency and precision, resulting in engineered cells that do not fully replicate the desired identity or functional output. This applies to different methods of cell programming, which face similar challenges that hinder progress and delay the achievement of a more favourable outcome. However, recent technological and analytical breakthroughs have provided us with unprecedented opportunities to advance the way we programme cell fate. The Company of Biologists' 2023 workshop on 'Novel Technologies for Programming Human Cell Fate' brought together experts in human cell fate engineering and experts in single-cell genomics, manipulation and characterisation of cells on a single (sub)cellular level. Here, we summarise the main points that emerged during the workshop's themed discussions. Furthermore, we provide specific examples highlighting the current state of the field as well as its trajectory, offering insights into the potential outcomes resulting from the application of these breakthrough technologies in precisely engineering the identity and function of clinically valuable human cells.

Proliferation history and transcription factor levels drive direct conversion

The sparse and stochastic nature of reprogramming has obscured our understanding of how transcription factors drive cells to new identities. To overcome this limit, we developed a compact, portable reprogramming system that increases direct conversion of fibroblasts to motor neurons by two orders of magnitude. We show that subpopulations with different reprogramming potentials are distinguishable by proliferation history. By controlling for proliferation history and titrating each transcription factor, we find that conversion correlates with levels of the pioneer transcription factor Ngn2, whereas conversion shows a biphasic response to Lhx3. Increasing the proliferation rate of adult human fibroblasts generates morphologically mature, induced motor neurons at high rates. Using compact, optimized, polycistronic cassettes, we generate motor neurons that graft with the murine central nervous system, demonstrating the potential for in vivo therapies.