Inference of differentiation time for single cell transcriptomes using cell population reference data

scMDCL: A Deep Collaborative Contrastive Learning Framework for Matched Single-Cell Multiomics Data Clustering

Single-cell multiomics clustering integrates multiple omics data to analyze cellular heterogeneity and is crucial for uncovering complex biological processes and disease mechanisms. However, existing matched single-cell multiomics clustering methods often neglect the full utilization of intercellular relationships and the interactions and synergy between features from different omics, leading to suboptimal clustering performance. In this paper, we propose a deep collaborative contrastive learning framework for matched single-cell multiomics data clustering, named scMDCL. This framework fully leverages intercell relationships while enhancing feature interactions among identical cells across different omics data, thereby facilitating efficient clustering of multiomics data. Specifically, to fully utilize the topological information between cells, a graph autoencoder and a feature information enhancement module are designed for different omics, enabling the extraction and augmentation of cell features. Additionally, contrastive learning techniques are employed to strengthen the interactions among the different omics features of the same cell. Ultimately, multiomics deep collaborative clustering modules are utilized to achieve single-cell multiomics clustering. Extensive experiments conducted on nine publicly available single-cell multiomics datasets demonstrate the superior performance of the proposed framework in integrating multiomics data for clustering tasks.

scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology has become increasingly vital. This study explores the application of deep learning to single-cell data clustering, with a particular focus on managing sparse, high-dimensional data.

RESULTS

We propose the SMD deep learning model, which integrates nonlinear dimensionality reduction techniques with a porous dilated attention gate component. Built upon a convolutional autoencoder and informed by the negative binomial distribution, the SMD model efficiently captures essential cell clustering features and dynamically adjusts feature weights. Comprehensive evaluation on both public datasets and proprietary osteosarcoma data highlights the SMD model's efficacy in achieving precise classifications for single-cell data clustering, showcasing its potential for advanced transcriptomic analysis.

CONCLUSION

This study underscores the potential of deep learning-specifically the SMD model-in advancing single-cell RNA sequencing data analysis. By integrating innovative computational techniques, the SMD model provides a powerful framework for unraveling cellular complexities, enhancing our understanding of biological processes, and elucidating disease mechanisms. The code is available from  https://github.com/xiaoxuc/scSMD .

scGRN-Entropy: Inferring cell differentiation trajectories using single-cell data and gene regulation network-based transfer entropy

Research on cell differentiation facilitates a deeper understanding of the fundamental processes of life, elucidates the intrinsic mechanisms underlying diseases such as cancer, and advances the development of therapeutics and precision medicine. Existing methods for inferring cell differentiation trajectories from single-cell RNA sequencing (scRNA-seq) data primarily rely on static gene expression data to measure distances between cells and subsequently infer pseudotime trajectories. In this work, we introduce a novel method, scGRN-Entropy, for inferring cell differentiation trajectories and pseudotime from scRNA-seq data. Unlike existing approaches, scGRN-Entropy improves inference accuracy by incorporating dynamic changes in gene regulatory networks (GRN). In scGRN-Entropy, an undirected graph representing state transitions between cells is constructed by integrating both static relationships in gene expression space and dynamic relationships in the GRN space. The edges of the undirected graph are then refined using pseudotime inferred based on cell entropy in the GRN space. Finally, the Minimum Spanning Tree (MST) algorithm is applied to derive the cell differentiation trajectory. We validate the accuracy of scGRN-Entropy on eight different real scRNA-seq datasets, demonstrating its superior performance in inferring cell differentiation trajectories through comparative analysis with existing state-of-the-art methods.

scSwinFormer: A Transformer-Based Cell-Type Annotation Method for scRNA-Seq Data Using Smooth Gene Embedding and Global Features

Single-cell omics techniques have made it possible to analyze individual cells in biological samples, providing us with a more detailed understanding of cellular heterogeneity and biological systems. Accurate identification of cell types is critical for single-cell RNA sequencing (scRNA-seq) analysis. However, scRNA-seq data are usually high dimensional and sparse, posing a great challenge to analyze scRNA-seq data. Existing cell-type annotation methods are either constrained in modeling scRNA-seq data or lack consideration of long-term dependencies of characterized genes. In this work, we developed a Transformer-based deep learning method, scSwinFormer, for the cell-type annotation of large-scale scRNA-seq data. Sequence modeling of scRNA-seq data is performed using the smooth gene embedding module, and then, the potential dependencies of genes are captured by the self-attention module. Subsequently, the global information inherent in scRNA-seq data is synthesized using the Cell Token, thereby facilitating accurate cell-type annotation. We evaluated the performance of our model against current state-of-the-art scRNA-seq cell-type annotation methods on multiple real data sets. ScSwinFormer outperforms the current state-of-the-art scRNA-seq cell-type annotation methods in both external and benchmark data set experiments.

Thermal facial image analyses reveal quantitative hallmarks of aging and metabolic diseases

Although human core body temperature is known to decrease with age, the age dependency of facial temperature and its potential to indicate aging rate or aging-related diseases remains uncertain. Here, we collected thermal facial images of 2,811 Han Chinese individuals 20-90 years old, developed the ThermoFace method to automatically process and analyze images, and then generated thermal age and disease prediction models. The ThermoFace deep learning model for thermal facial age has a mean absolute deviation of about 5 years in cross-validation and 5.18 years in an independent cohort. The difference between predicted and chronological age is highly associated with metabolic parameters, sleep time, and gene expression pathways like DNA repair, lipolysis, and ATPase in the blood transcriptome, and it is modifiable by exercise. Consistently, ThermoFace disease predictors forecast metabolic diseases like fatty liver with high accuracy (AUC > 0.80), with predicted disease probability correlated with metabolic parameters.

Application of AI in biological age prediction

The development of anti-aging interventions requires quantitative measurement of biological age. Machine learning models, known as "aging clocks," are built by leveraging diverse aging biomarkers that vary across lifespan to predict biological age. In addition to traditional aging clocks harnessing epigenetic signatures derived from bulk samples, emerging technologies allow the biological age estimating at single-cell level to dissect cellular diversity in aging tissues. Moreover, imaging-based aging clocks are increasingly employed with the advantage of non-invasive measurement, making it suitable for large-scale human cohort studies. To fully capture the features in the ever-growing multi-modal and high-dimensional aging-related data and uncover disease associations, deep-learning based approaches, which are effective to learn complex and non-linear relationships without relying on pre-defined features, are increasingly applied. The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.

The ticking of aging clocks

Computational models that measure biological age and aging rate regardless of chronological age are called aging clocks. The underlying counting mechanisms of the intrinsic timers of these clocks are still unclear. Molecular mediators and determinants of aging rate point to the key roles of DNA damage, epigenetic drift, and inflammation. Persistent DNA damage leads to cellular senescence and the senescence-associated secretory phenotype (SASP), which induces cytotoxic immune cell infiltration; this further induces DNA damage through reactive oxygen and nitrogen species (RONS). I discuss the possibility that DNA damage (or the response to it, including epigenetic changes) is the fundamental counting unit of cell cycles and cellular senescence, that ultimately accounts for cell composition changes and functional decline in tissues, as well as the key intervention points.

scMIC: A Deep Multi-Level Information Fusion Framework for Clustering Single-Cell Multi-Omics Data

Cell type identification is a crucial step towards the study of cellular heterogeneity and biological processes. Advances in single-cell sequencing technology have enabled the development of a variety of clustering methods for cell type identification. However, most of existing methods are designed for clustering single omic data such as single-cell RNA-sequencing (scRNA-seq) data. The accumulation of single-cell multi-omics data provides a great opportunity to integrate different omics data for cell clustering, but also raise new computational challenges for existing methods. How to integrate multi-omics data and leverage their consensus and complementary information to improve the accuracy of cell clustering still remains a challenge. In this study, we propose a new deep multi-level information fusion framework, named scMIC, for clustering single-cell multi-omics data. Our model can integrate the attribute information of cells and the potential structural relationship among cells from local and global levels, and reduce redundant information between different omics from cell and feature levels, leading to more discriminative representations. Moreover, the proposed multiple collaborative supervised clustering strategy is able to guide the learning process of the core encoding part by learning the high-confidence target distribution, which facilitates the interaction between the clustering part and the representation learning part, as well as the information exchange between omics, and finally obtain more robust clustering results. Experiments on seven single-cell multi-omics datasets show the superiority of scMIC over existing state-of-the-art methods.

BLTSA: pseudotime prediction for single cells by branched local tangent space alignment

MOTIVATION

The development of single-cell RNA sequencing (scRNA-seq) technology makes it possible to study the cellular dynamic processes such as cell cycle and cell differentiation. Due to the difficulties in generating genuine time-series scRNA-seq data, it is of great importance to computationally infer the pseudotime of the cells along differentiation trajectory based on their gene expression patterns. The existing pseudotime prediction methods often suffer from the high level noise of single-cell data, thus it is still necessary to study the single-cell trajectory inference methods.

RESULTS

In this study, we propose a branched local tangent space alignment (BLTSA) method to infer single-cell pseudotime for multi-furcation trajectories. By assuming that single cells are sampled from a low-dimensional self-intersecting manifold, BLTSA first identifies the tip and branching cells in the trajectory based on cells' local Euclidean neighborhoods. Local coordinates within the tangent spaces are then determined by each cell's local neighborhood after clustering all the cells to different branches iteratively. The global coordinates for all the single cells are finally obtained by aligning the local coordinates based on the tangent spaces. We evaluate the performance of BLTSA on four simulation datasets and five real datasets. The experimental results show that BLTSA has obvious advantages over other comparison methods.

AVAILABILITY AND IMPLEMENTATION

R codes are available at https://github.com/LiminLi-xjtu/BLTSA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network

The advances in single-cell ribonucleic acid sequencing (scRNA-seq) allow researchers to explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a prerequisite in scRNA-seq analysis since it can recognize cell identities. However, the high dimensionality, noises and significant sparsity of scRNA-seq data have made it a big challenge. Although many methods have emerged, they still fail to fully explore the intrinsic properties of cells and the relationship among cells, which seriously affects the downstream clustering performance. Here, we propose a new deep contrastive clustering algorithm called scDCCA. It integrates a denoising auto-encoder and a dual contrastive learning module into a deep clustering framework to extract valuable features and realize cell clustering. Specifically, to better characterize and learn data representations robustly, scDCCA utilizes a denoising Zero-Inflated Negative Binomial model-based auto-encoder to extract low-dimensional features. Meanwhile, scDCCA incorporates a dual contrastive learning module to capture the pairwise proximity of cells. By increasing the similarities between positive pairs and the differences between negative ones, the contrasts at both the instance and the cluster level help the model learn more discriminative features and achieve better cell segregation. Furthermore, scDCCA joins feature learning with clustering, which realizes representation learning and cell clustering in an end-to-end manner. Experimental results of 14 real datasets validate that scDCCA outperforms eight state-of-the-art methods in terms of accuracy, generalizability, scalability and efficiency. Cell visualization and biological analysis demonstrate that scDCCA significantly improves clustering and facilitates downstream analysis for scRNA-seq data. The code is available at https://github.com/WJ319/scDCCA.

Application of Single-Cell RNA Sequencing in Ovarian Development

The ovary is a female reproductive organ that plays a key role in fertility and the maintenance of endocrine homeostasis, which is of great importance to women's health. It is characterized by a high heterogeneity, with different cellular subpopulations primarily containing oocytes, granulosa cells, stromal cells, endothelial cells, vascular smooth muscle cells, and diverse immune cell types. Each has unique and important functions. From the fetal period to old age, the ovary experiences continuous structural and functional changes, with the gene expression of each cell type undergoing dramatic changes. In addition, ovarian development strongly relies on the communication between germ and somatic cells. Compared to traditional bulk RNA sequencing techniques, the single-cell RNA sequencing (scRNA-seq) approach has substantial advantages in analyzing individual cells within an ever-changing and complicated tissue, classifying them into cell types, characterizing single cells, delineating the cellular developmental trajectory, and studying cell-to-cell interactions. In this review, we present single-cell transcriptome mapping of the ovary, summarize the characteristics of the important constituent cells of the ovary and the critical cellular developmental processes, and describe key signaling pathways for cell-to-cell communication in the ovary, as revealed by scRNA-seq. This review will undoubtedly improve our understanding of the characteristics of ovarian cells and development, thus enabling the identification of novel therapeutic targets for ovarian-related diseases.

Effectively Clustering Single Cell RNA Sequencing Data by Sparse Representation

Clustering analysis has been widely used in analyzing single-cell RNA-sequencing (scRNA-seq) data to study various biological problems at cellular level. Although a number of scRNA-seq data clustering methods have been developed, most of them evaluate the similarity of pairwise cells while ignoring the global relationships among cells, which sometimes cannot effectively capture the latent structure of cells. In this paper, we propose a new clustering method SPARC for scRNA-seq data. The most important feature of SPARC is a novel similarity metric that uses the sparse representation coefficients of each cell in terms of the other cells to measure the relationships among cells. In addition, we develop an outlier detection method to help parameter selection in SPARC. We compare SPARC with nine existing scRNA-seq data clustering methods on twelve real datasets. Experimental results show that SPARC achieves the state of the art performance. By further analyzing the cell similarity data derived from sparse representations, we find that SPARC is much more effective in mining high quality clusters of scRNA-seq data than two traditional similarity metrics. In conclusion, this study provides a new way to effectively cluster scRNA-seq data and achieves more accurate clustering results than the state of art methods.

A guiding role of the Arabidopsis circadian clock in cell differentiation revealed by time-series single-cell RNA sequencing

Circadian rhythms and progression of cell differentiation are closely coupled in multicellular organisms. However, whether establishment of circadian rhythms regulates cell differentiation or vice versa has not been elucidated due to technical limitations. Here, we exploit high cell fate plasticity of plant cells to perform single-cell RNA sequencing during the entire process of cell differentiation. By analyzing reconstructed actual time series of the differentiation processes at single-cell resolution using a method we developed (PeakMatch), we find that the expression profile of clock genes is changed prior to cell differentiation, including induction of the clock gene LUX ARRYTHMO (LUX). ChIP sequencing analysis reveals that LUX induction in early differentiating cells directly targets genes involved in cell-cycle progression to regulate cell differentiation. Taken together, these results not only reveal a guiding role of the plant circadian clock in cell differentiation but also provide an approach for time-series analysis at single-cell resolution.

Langley M, Fisher CL, Aguilar-Hidalgo D, Shukla S, Yachie-Kinoshita A, Hughes M, M. McNagny K, Zandstra PW. IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data

The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to scRNA-seq datasets from early mouse T-cell and red blood cell development, and show that the platform can infer overall over 74% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of known gene perturbations. Finally, we implement an IQCELL gene selection pipeline that allows us to identify candidate genes, without prior knowledge. We demonstrate that GRN simulations based on the inferred set yield results similar to the original curated lists. In summary, the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems.

A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data

Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods' performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.

Interspecies chimeric conditions affect the developmental rate of human pluripotent stem cells

Human pluripotent stem cells hold significant promise for regenerative medicine. However, long differentiation protocols and immature characteristics of stem cell-derived cell types remain challenges to the development of many therapeutic applications. In contrast to the slow differentiation of human stem cells in vitro that mirrors a nine-month gestation period, mouse stem cells develop according to a much faster three-week gestation timeline. Here, we tested if co-differentiation with mouse pluripotent stem cells could accelerate the differentiation speed of human embryonic stem cells. Following a six-week RNA-sequencing time course of neural differentiation, we identified 929 human genes that were upregulated earlier and 535 genes that exhibited earlier peaked expression profiles in chimeric cell cultures than in human cell cultures alone. Genes with accelerated upregulation were significantly enriched in Gene Ontology terms associated with neurogenesis, neuron differentiation and maturation, and synapse signaling. Moreover, chimeric mixed samples correlated with in utero human embryonic samples earlier than human cells alone, and acceleration was dose-dependent on human-mouse co-culture ratios. The altered gene expression patterns and developmental rates described in this report have implications for accelerating human stem cell differentiation and the use of interspecies chimeric embryos in developing human organs for transplantation.

SCCLRR: A Robust Computational Method for Accurate Clustering Single Cell RNA-Seq Data

Single-cell RNA transcriptome data present a tremendous opportunity for studying the cellular heterogeneity. Identifying subpopulations based on scRNA-seq data is a hot topic in recent years, although many researchers have been focused on designing elegant computational methods for identifying new cell types; however, the performance of these methods is still unsatisfactory due to the high dimensionality, sparsity and noise of scRNA-seq data. In this study, we propose a new cell type detection method by learning a robust and accurate similarity matrix, named SCCLRR. The method simultaneously captures both global and local intrinsic properties of data based on a low rank representation (LRR) framework mathematical model. The integrated normalized Euclidean distance and cosine similarity are used to balance the intrinsic linear and nonlinear manifold of data in the local regularization term. To solve the non-convex optimization model, we present an iterative optimization procedure using the alternating direction method of multipliers (ADMM) algorithm. We evaluate the performance of the SCCLRR method on nine real scRNA-seq datasets and compare it with seven state-of-the-art methods. The simulation results show that the SCCLRR outperforms other methods and is robust and effective for clustering scRNA-seq data. (The code of SCCLRR is free available for academic https://github.com/wzhangwhu/SCCLRR).

Towards systems tissue engineering: Elucidating the dynamics, spatial coordination, and individual cells driving emergent behaviors

Biomaterial systems have enabled the in vitro production of complex, emergent tissue behaviors that were not possible with conventional two-dimensional culture systems, allowing for analysis of both normal development and disease processes. We propose that the path towards developing the design parameters for biomaterial systems lies with identifying the molecular drivers of emergent behavior through leveraging technological advances in systems biology, including single cell omics, genetic engineering, and high content imaging. This growing research opportunity at the intersection of the fields of tissue engineering and systems biology - systems tissue engineering - can uniquely interrogate the mechanisms by which complex tissue behaviors emerge with the potential to capture the contribution of i) dynamic regulation of tissue development and dysregulation, ii) single cell heterogeneity and the function of rare cell types, and iii) the spatial distribution and structure of individual cells and cell types within a tissue. By leveraging advances in both biological and materials data science, systems tissue engineering can facilitate the identification of biomaterial design parameters that will accelerate basic science discovery and translation.

A 3D Atlas of Hematopoietic Stem and Progenitor Cell Expansion by Multi-dimensional RNA-Seq Analysis

In vertebrates, hematopoiesis occurring in different niches is orchestrated by intrinsic and extrinsic regulators. Previous studies have revealed numerous linear and planar regulatory mechanisms. However, a multi-dimensional transcriptomic atlas of any given hematopoietic organ has not yet been established. Here, we use multiple RNA sequencing (RNA-seq) approaches, including cell type-specific, temporal bulk RNA-seq, in vivo GEO-seq, and single-cell RNA-seq (scRNA-seq), to characterize the detailed spatiotemporal transcriptome during hematopoietic stem and progenitor cell (HSPC) expansion in the caudal hematopoietic tissue (CHT) of zebrafish. Combinatorial expression profiling reveals that, in the CHT niche, HSPCs and their neighboring supporting cells are co-regulated by shared signaling pathways and intrinsic factors, such as integrin signaling and Smchd1. Moreover, scRNA-seq analysis unveils the strong association between cell cycle status and HSPC differentiation. Taken together, we report a global transcriptome landscape that provides valuable insights and a rich resource to understand HSPC expansion in an intact vertebrate hematopoietic organ.

The Contribution of lincRNAs at the Interface between Cell Cycle Regulation and Cell State Maintenance

Cell cycle progression is controlled by the interplay of established cell cycle regulators. Changes in these regulators' activity underpin differences in cell cycle kinetics between cell types. We investigated whether long intergenic noncoding RNAs (lincRNAs) contribute to embryonic stem cell cycle adaptations. Using single-cell RNA sequencing data for mouse embryonic stem cells (mESCs) staged as G1, S, or G2/M we found differentially expressed lincRNAs are enriched among cell cycle-regulated genes. These lincRNAs (CC-lincRNAs) are co-expressed with genes involved in cell cycle regulation. We tested the impact of two CC-lincRNA candidates and show using CRISPR activation that increasing their expression is associated with deregulated cell cycle progression. Interestingly, CC-lincRNAs are often differentially expressed between G1 and S, their promoters are enriched in pluripotency transcription factor (TF) binding sites, and their transcripts are frequently co-regulated with genes involved in the maintenance of pluripotency, suggesting a contribution of CC-lincRNAs to mESC cell cycle adaptations.

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Genes differentially expressed between mESC cell cycle stages are enriched in lincRNAs

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CC-lincRNAs are co-expressed with cell cycle and pluripotency genes

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CC-lincRNAs are often mESC specific and their promoters enriched in pluripotency TFs

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Upregulation of two CC-lincRNAs results in deregulated mESC cell cycle progression

Novel insights into cell cycle regulation of cell fate determination

The stem/progenitor cell has long been regarded as a central cell type in development, homeostasis, and regeneration, largely owing to its robust self-renewal and multilineage differentiation abilities. The balance between self-renewal and stem/progenitor cell differentiation requires the coordinated regulation of cell cycle progression and cell fate determination. Extensive studies have demonstrated that cell cycle states determine cell fates, because cells in different cell cycle states are characterized by distinct molecular features and functional outputs. Recent advances in high-resolution epigenome profiling, single-cell transcriptomics, and cell cycle reporter systems have provided novel insights into the cell cycle regulation of cell fate determination. Here, we review recent advances in cell cycle-dependent cell fate determination and functional heterogeneity, and the application of cell cycle manipulation for cell fate conversion. These findings will provide insight into our understanding of cell cycle regulation of cell fate determination in this field, and may facilitate its potential application in translational medicine.

Cell lineage inference from SNP and scRNA-Seq data

Several recent studies focus on the inference of developmental and response trajectories from single cell RNA-Seq (scRNA-Seq) data. A number of computational methods, often referred to as pseudo-time ordering, have been developed for this task. Recently, CRISPR has also been used to reconstruct lineage trees by inserting random mutations. However, both approaches suffer from drawbacks that limit their use. Here, we develop a method to detect significant, cell type specific, sequence mutations from scRNA-Seq data. We show that only a few mutations are enough for reconstructing good branching models. Integrating these mutations with expression data further improves the accuracy of the reconstructed models. As we show, the majority of mutations we identify are likely RNA editing events indicating that such information can be used to distinguish cell types.

Trajectory Algorithms to Infer Stem Cell Fate Decisions

Single-cell trajectory analysis is an active research area in single-cell genomics aiming at developing sophisticated algorithms to reconstruct complex cell-state transition trajectories. Here, we present a step-by-step protocol to use CellRouter, a multifaceted single-cell analysis platform that integrates subpopulation identification, gene regulatory networks, and trajectory inference to precisely and flexibly reconstruct complex single-cell trajectories. Subpopulations are either user-defined or identified by a graph-clustering approach in which a k-nearest neighbor graph (kNN) is created from cell-to-cell distances in a low-dimensional embedding. Edges in this graph are weighted by network similarity metrics (e.g., Jaccard index) to robustly encode phenotypic relatedness, creating a representation of single-cell transcriptomes suitable for community detection algorithms to identify clusters of densely connected cells. This subpopulation structure represents a map of putative cell-state transitions. CellRouter implements a flow network algorithm to explore this map and reconstruct cell-state transitions in complex single-cell, multidimensional omics datasets. We describe a step-by-step application of CellRouter to hematopoietic stem and progenitor cell differentiation toward four major lineages-erythrocytes, megakaryocytes, monocytes, and granulocytes-to demonstrate key components of CellRouter for single-cell trajectory analysis.

Regulatory network characterization in development: challenges and opportunities

Embryonic development and stem cell differentiation, during which coordinated cell fate specification takes place in a spatial and temporal context, serve as a paradigm for studying the orderly assembly of gene regulatory networks (GRNs) and the fundamental mechanism of GRNs in driving lineage determination. However, knowledge of reliable GRN annotation for dynamic development regulation, particularly for unveiling the complex temporal and spatial architecture of tissue stem cells, remains inadequate. With the advent of single-cell RNA sequencing technology, elucidating GRNs in development and stem cell processes poses both new challenges and unprecedented opportunities. This review takes a snapshot of some of this work and its implication in the regulative nature of early mammalian development and specification of the distinct cell types during embryogenesis.

Single-cell RNA-sequencing reveals the existence of naive and primed pluripotency in pre-implantation rhesus monkey embryos

Naive pluripotency exists in epiblast cells of mouse pre-implantation embryos. However, whether the naive pluripotency is transient or nonexistent in primate embryos remains unclear. Using RNA-seq in single blastomeres from 16-cell embryos through to hatched blastocysts of rhesus monkey, we constructed the lineage segregation roadmap in which the specification of trophectoderm, epiblast, and primitive endoderm is initiated simultaneously at the early blastocyst stage. Importantly, we uncovered the existence of distinct pluripotent states in monkey pre-implantation embryos. At the early- and middle-blastocyst stages, the epiblast cells have the transcriptome features of naive pluripotency, whereas they display a continuum of primed pluripotency characteristics at the late and hatched blastocyst stages. Moreover, we identified potential regulators that might play roles in the transition from naive to primed pluripotency. Thus, our study suggests the transient existence of naive pluripotency in primates and proposes an ideal time window for derivation of primate embryonic stem cells with naive pluripotency.

NeuroSystematics and Periodic System of Neurons: Model vs Reference Species at Single-Cell Resolution

There is more than one way to develop neuronal complexity, and animals frequently use different molecular toolkits to achieve similar functional outcomes (=convergent evolution). Neurons are different not only because they have different functions, but also because neurons and circuits have different genealogies, and perhaps independent origins at the broadest scale from ctenophores and cnidarians to cephalopods and primates. By combining modern phylogenomics, single-neuron sequencing (scRNA-seq), machine learning, single-cell proteomics, and metabolomic across Metazoa, it is possible to reconstruct the evolutionary histories of neurons tracing them to ancestral secretory cells. Comparative data suggest that neurons, and perhaps synapses, evolved at least 2-3 times (in ctenophore, cnidarian and bilateral lineages) during ∼600 million years of animal evolution. There were also several independent events of the nervous system centralization either from a common bilateral/cnidarian ancestor without the bona fide neurons or from the urbilaterian with diffuse, nerve-net type nervous system. From the evolutionary standpoint, (i) a neuron should be viewed as a functional rather than a genetic character, and (ii) any given neural system might be chimeric and composed of different cell lineages with distinct origins and evolutionary histories. The identification of distant neural homologies or examples of convergent evolution among 34 phyla will not only allow the reconstruction of neural systems' evolution but together with single-cell "omic" approaches the proposed synthesis would lead to the "Periodic System of Neurons" with predictive power for neuronal phenotypes and plasticity. Such a phylogenetic classification framework of Neuronal Systematics (NeuroSystematics) might be a conceptual analog of the Periodic System of Chemical Elements. scRNA-seq profiling of all neurons in an entire brain or Brain-seq is now fully achievable in many nontraditional reference species across the entire animal kingdom. Arguably, marine animals are the most suitable for the proposed tasks because the world oceans represent the greatest taxonomic and body-plan diversity.