Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

Cancer Stemness Online: A Resource for Investigating Cancer Stemness and Associations with Immune Response

Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem cell-like features, which are potential culprits of immunotherapy resistance. Although the state-of-the-art predictive computational methods have facilitated the prediction of cancer stemness, there remains a lack of efficient resources to accommodate various usage requirements. Here, we present the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at both bulk and single-cell levels. This resource integrates eight robust predictive algorithms as well as 27 signature gene sets associated with cancer stemness for predicting stemness scores. Downstream analyses were performed from five distinct aspects: identifying the signature genes of cancer stemness; exploring the associations with cancer hallmarks and cellular states; exploring the associations with immune response and the communications with immune cells; investigating the contributions to patient survival; and performing a robustness analysis of cancer stemness among different methods. Moreover, the pre-calculated cancer stemness atlas for more than 40 cancer types can be accessed by users. Both the tables and diverse visualizations of the analytical results are available for download. Together, Cancer Stemness Online is a powerful resource for scoring cancer stemness and expanding downstream functional interpretation, including immune response and cancer hallmarks. Cancer Stemness Online is freely accessible at http://bio-bigdata.hrbmu.edu.cn/CancerStemnessOnline.

SCTC: inference of developmental potential from single-cell transcriptional complexity

Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.

RNA structure profiling at single-cell resolution reveals new determinants of cell identity

RNA structure is critical for multiple steps in gene regulation. However, how the structures of transcripts differ both within and between individual cells is unknown. Here we develop a SHAPE-inspired method called single-cell structure probing of RNA transcripts that enables simultaneous determination of transcript secondary structure and abundance at single-cell resolution. We apply single-cell structure probing of RNA transcripts to human embryonic stem cells and differentiating neurons. Remarkably, RNA structure is more homogeneous in human embryonic stem cells compared with neurons, with the greatest homogeneity found in coding regions. More extensive heterogeneity is found within 3' untranslated regions and is determined by specific RNA-binding proteins. Overall RNA structure profiles better discriminate cell type identity and differentiation stage than gene expression profiles alone. We further discover a cell-type variable region of 18S ribosomal RNA that is associated with cell cycle and translation control. Our method opens the door to the systematic characterization of RNA structure-function relationships at single-cell resolution.

Forskolin-driven conversion of human somatic cells into induced neurons through regulation of the cAMP-CREB1-JNK signaling

Human somatic cells can be reprogrammed into neuron cell fate through regulation of a single transcription factor or application of small molecule cocktails. Methods: Here, we report that forskolin efficiently induces the conversion of human somatic cells into induced neurons (FiNs). Results: A large population of neuron-like phenotype cells was observed as early as 24-36 h post-induction. There were >90% TUJ1-, >80% MAP2-, and >80% NEUN-positive neurons at 5 days post-induction. Multiple subtypes of neurons were present among TUJ1-positive cells, including >60% cholinergic, >20% glutamatergic, >10% GABAergic, and >5% dopaminergic neurons. FiNs exhibited typical neural electrophysiological activity in vitro and the ability to survive in vitro and in vivo more than 2 months. Mechanistically, forskolin functions in FiN reprogramming by regulating the cAMP-CREB1-JNK signals, which upregulates cAMP-CREB1 expression and downregulates JNK expression. Conclusion: Overall, our studies identify a safer and efficient single-small-molecule-driven reprogramming approach for induced neuron generation and reveal a novel regulatory mechanism of neuronal cell fate acquisition.

scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

Computationally defined and in vitro validated putative genomic safe harbour loci for transgene expression in human cells

Selection of the target site is an inherent question for any project aiming for directed transgene integration. Genomic safe harbour (GSH) loci have been proposed as safe sites in the human genome for transgene integration. Although several sites have been characterised for transgene integration in the literature, most of these do not meet criteria set out for a GSH and the limited set that do have not been characterised extensively. Here, we conducted a computational analysis using publicly available data to identify 25 unique putative GSH loci that reside in active chromosomal compartments. We validated stable transgene expression and minimal disruption of the native transcriptome in three GSH sites in vitro using human embryonic stem cells (hESCs) and their differentiated progeny. Furthermore, for easy targeted transgene expression, we have engineered constitutive landing pad expression constructs into the three validated GSH in hESCs.

SPIDE: A single cell potency inference method based on the local cell-specific network entropy

For a given single cell RNA-seq data, it is critical to pinpoint key cellular stages and quantify cells' differentiation potency along a differentiation pathway in a time course manner. Currently, several methods based on the entropy of gene functions or PPI network have been proposed to solve the problem. Nevertheless, these methods still suffer from the inaccurate interactions and noises originating from scRNA-seq profile. In this study, we proposed a cell potency inference method based on cell-specific network entropy, called SPIDE. SPIDE introduces the local weighted cell-specific network for each cell to maintain cell heterogeneity and calculates the entropy by incorporating gene expression with network structure. In this study, we compared three cell entropy estimation models on eight scRNA-Seq datasets. The results show that SPIDE obtains consistent conclusions with real cell differentiation potency on most datasets. Moreover, SPIDE accurately recovers the continuous changes of potency during cell differentiation and significantly correlates with the stemness of tumor cells in Colorectal cancer. To conclude, our study provides a universal and accurate framework for cell entropy estimation, which deepens our understanding of cell differentiation, the development of diseases and other related biological research.

SGAE: single-cell gene association entropy for revealing critical states of cell transitions during embryonic development

The critical point or pivotal threshold of cell transition occurs in early embryonic development when cell differentiation culminates in its transition to specific cell fates, at which the cell population undergoes an abrupt and qualitative shift. Revealing such critical points of cell transitions can track cellular heterogeneity and shed light on the molecular mechanisms of cell differentiation. However, precise detection of critical state transitions proves challenging when relying on single-cell RNA sequencing data due to their inherent sparsity, noise, and heterogeneity. In this study, diverging from conventional methods like differential gene analysis or static techniques that emphasize classification of cell types, an innovative computational approach, single-cell gene association entropy (SGAE), is designed for the analysis of single-cell RNA-seq data and utilizes gene association information to reveal critical states of cell transitions. More specifically, through the translation of gene expression data into local SGAE scores, the proposed SGAE can serve as an index to quantitatively assess the resilience and critical properties of genetic regulatory networks, consequently detecting the signal of cell transitions. Analyses of five single-cell datasets for embryonic development demonstrate that the SGAE method achieves better performance in facilitating the characterization of a critical phase transition compared with other existing methods. Moreover, the SGAE value can effectively discriminate cellular heterogeneity over time and performs well in the temporal clustering of cells. Besides, biological functional analysis also indicates the effectiveness of the proposed approach.

P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network

Although many single-cell computational methods proposed use gene expression as input, recent studies show that replacing 'unstable' gene expression with 'stable' gene-gene associations can greatly improve the performance of downstream analysis. To obtain accurate gene-gene associations, conditional cell-specific network method (c-CSN) filters out the indirect associations of cell-specific network method (CSN) based on the conditional independence of statistics. However, when there are strong connections in networks, the c-CSN suffers from false negative problem in network construction. To overcome this problem, a new partial cell-specific network method (p-CSN) based on the partial independence of statistics is proposed in this paper, which eliminates the singularity of the c-CSN by implicitly including direct associations among estimated variables. Based on the p-CSN, single-cell network entropy (scNEntropy) is further proposed to quantify cell state. The superiorities of our method are verified on several datasets. (i) Compared with traditional gene regulatory network construction methods, the p-CSN constructs partial cell-specific networks, namely, one cell to one network. (ii) When there are strong connections in networks, the p-CSN reduces the false negative probability of the c-CSN. (iii) The input of more accurate gene-gene associations further optimizes the performance of downstream analyses. (iv) The scNEntropy effectively quantifies cell state and reconstructs cell pseudo-time.

Accurate integration of multiple heterogeneous single-cell RNA-seq data sets by learning contrastive biological variation

For most biological and medical applications of single-cell transcriptomics, an integrative study of multiple heterogeneous single-cell RNA sequencing (scRNA-seq) data sets is crucial. However, present approaches are unable to integrate diverse data sets from various biological conditions effectively because of the confounding effects of biological and technical differences. We introduce single-cell integration (scInt), an integration method based on accurate, robust cell-cell similarity construction and unified contrastive biological variation learning from multiple scRNA-seq data sets. scInt provides a flexible and effective approach to transfer knowledge from the already integrated reference to the query. We show that scInt outperforms 10 other cutting-edge approaches using both simulated and real data sets, particularly in the case of complex experimental designs. Application of scInt to mouse developing tracheal epithelial data shows its ability to integrate development trajectories from different developmental stages. Furthermore, scInt successfully identifies functionally distinct condition-specific cell subpopulations in single-cell heterogeneous samples from a variety of biological conditions.

Cell Features Reconstruction from Gene Association Network of Single Cell

Gene expression as an unstable form of cell characterization has been widely used for single-cell analyses. Although there are cell-specific networks (CSN) to explore stable gene associations within a single cell, the amount of information in CSN is huge and there is no method to measure the interaction level between genes. Therefore, this paper presents a two-level approach to reconstructing single-cell features, which transforms the original gene expression feature into the gene ontology feature and gene interaction feature. Specifically, we first squeeze all CSNs into a cell network feature matrix (CNFM) by fusing the global position and neighborhood influence of genes. Next, we propose a computational method of gene gravitation based on CNFM to quantify the extent of gene-gene interaction, and we can construct a gene gravitation network for single cells. Finally, we further design a novel index of gene gravitation entropy to quantitatively evaluate the level of single-cell differentiation. The experiments on eight different scRNA-seq datasets show the effectiveness and broad application prospects of our method.

Accurate Estimation of Single-Cell Differentiation Potency Based on Network Topology and Gene Ontology Information

One important task in single-cell analysis is to quantify the differentiation potential of single cells. Though various single-cell potency measures have been proposed, they are based on individual biological sources, thus not robust and reliable. It is still a challenge to combine multiple sources to generate a relatively reliable and robust measure to estimate differentiation. In this paper, we propose a New Centrality measure with Gene ontology information (NCG) to estimate single-cell potency. NCG is designed by combining network topology property with edge clustering coefficient, and gene function information using gene ontology function similarity scores. NCG distinguishes pluripotent cells from non-pluripotent cells with high accuracy, correctly ranks different cell types by their differentiation potency, tracks changes during the differentiation process, and constructs the lineage trajectory from human myoblasts into skeletal muscle cells. These indicate that NCG is a reliable and robust measure to estimate single-cell potency. NCG is anticipated to be a useful tool for identifying novel stem or progenitor cell phenotypes from single-cell RNA-Seq data. The source codes and datasets are available at https://github.com/Xinzhe-Ni/NCG.

Brain Neural Progenitors are New Predictive Biomarkers for Breast Cancer Hormonotherapy

Heterogeneity of the tumor microenvironment (TME) is one of the major causes of treatment resistance in breast cancer. Among TME components, nervous system role in clinical outcome has been underestimated. Identifying neuronal signatures associated with treatment response will help to characterize neuronal influence on tumor progression and identify new treatment targets. The search for hormonotherapy-predictive biomarkers was implemented by supervised machine learning (ML) analysis on merged transcriptomics datasets from public databases. ML-derived genes were investigated by pathway enrichment analysis, and potential gene signatures were curated by removing the variables that were not strictly nervous system specific. The predictive and prognostic abilities of the generated signatures were examined by Cox models, in the initial cohort and seven external cohorts. Generated signature performances were compared with 14 other published signatures, in both the initial and external cohorts. Underlying biological mechanisms were explored using deconvolution tools (CIBERSORTx and xCell). Our pipeline generated two nervous system-related signatures of 24 genes and 97 genes (NervSign24 and NervSign97). These signatures were prognostic and hormonotherapy-predictive, but not chemotherapy-predictive. When comparing their predictive performance with 14 published risk signatures in six hormonotherapy-treated cohorts, NervSign97 and NervSign24 were the two best performers. Pathway enrichment score and deconvolution analysis identified brain neural progenitor presence and perineural invasion as nervous system-related mechanisms positively associated with NervSign97 and poor clinical prognosis in hormonotherapy-treated patients. Transcriptomic profiling has identified two nervous system-related signatures that were validated in clinical samples as hormonotherapy-predictive signatures, meriting further exploration of neuronal component involvement in tumor progression.

Significance

The development of personalized and precision medicine is the future of cancer therapy. With only two gene expression signatures approved by FDA for breast cancer, we are in need of new ones that can reliably stratify patients for optimal treatment. This study provides two hormonotherapy-predictive and prognostic signatures that are related to nervous system in TME. It highlights tumor neuronal components as potential new targets for breast cancer therapy.

Genome-wide RNA structure changes during human neurogenesis modulate gene regulatory networks

The distribution, dynamics, and function of RNA structures in human development are under-explored. Here, we systematically assayed RNA structural dynamics and their relationship with gene expression, translation, and decay during human neurogenesis. We observed that the human ESC transcriptome is globally more structurally accessible than differentiated cells and undergoes extensive RNA structure changes, particularly in the 3' UTR. Additionally, RNA structure changes during differentiation are associated with translation and decay. We observed that RBP and miRNA binding is associated with RNA structural changes during early neuronal differentiation, and splicing is associated during later neuronal differentiation. Furthermore, our analysis suggests that RBPs are major factors in structure remodeling and co-regulate additional RBPs and miRNAs through structure. We demonstrated an example of this by showing that PUM2-induced structure changes on LIN28A enable miR-30 binding. This study deepens our understanding of the widespread and complex role of RNA-based gene regulation during human development.

Single-cell analysis reveals dynamic changes of neural cells in developing human spinal cord

During central nervous system development, neurogenesis and gliogenesis occur in an orderly manner to create precise neural circuitry. However, no systematic dataset of neural lineage development that covers both neurogenesis and gliogenesis for the human spinal cord is available. We here perform single-cell RNA sequencing of human spinal cord cells during embryonic and fetal stages that cover neuron generation as well as astrocytes and oligodendrocyte differentiation. We also map the timeline of sensory neurogenesis and gliogenesis in the spinal cord. We further identify a group of EGFR-expressing transitional glial cells with radial morphology at the onset of gliogenesis, which progressively acquires differentiated glial cell characteristics. These EGFR-expressing transitional glial cells exhibited a unique position-specific feature during spinal cord development. Cell crosstalk analysis using CellPhoneDB indicated that EGFR glial cells can persistently interact with other neural cells during development through Delta-Notch and EGFR signaling. Together, our results reveal stage-specific profiles and dynamics of neural cells during human spinal cord development.

Single-cell transcriptomics identifies master regulators of neurodegeneration in SOD1 ALS iPSC-derived motor neurons

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative condition characterized by the loss of motor neurons. We utilized single-cell transcriptomics to uncover dysfunctional pathways in degenerating motor neurons differentiated from SOD1 E100G ALS patient-derived induced pluripotent stem cells (iPSCs) and respective isogenic controls. Differential gene expression and network analysis identified activation of developmental pathways and core transcriptional factors driving the ALS motor neuron gene dysregulation. Specifically, we identified activation of SMAD2, a downstream mediator of the transforming growth factor β (TGF-β) signaling pathway as a key driver of SOD1 iPSC-derived motor neuron degeneration. Importantly, our analysis indicates that activation of TGFβ signaling may be a common mechanism shared between SOD1, FUS, C9ORF72, VCP, and sporadic ALS motor neurons. Our results demonstrate the utility of single-cell transcriptomics in mapping disease-relevant gene regulatory networks driving neurodegeneration in ALS motor neurons. We find that ALS-associated mutant SOD1 targets transcriptional networks that perturb motor neuron homeostasis.

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Single-cell transcriptomic analysis of SOD1 E100G ALS iPSC-derived motor neurons

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Mapping an ALS-relevant transcriptional network

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Upregulation of developmental programs in familial and sporadic ALS motor neurons

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Inhibition of TGFβ pathway improves SOD1 ALS iPSC-derived motor neuron survival

Robust integration of multiple single-cell RNA sequencing datasets using a single reference space

In many biological applications of single-cell RNA sequencing (scRNA-seq), an integrated analysis of data from multiple batches or studies is necessary. Current methods typically achieve integration using shared cell types or covariance correlation between datasets, which can distort biological signals. Here we introduce an algorithm that uses the gene eigenvectors from a reference dataset to establish a global frame for integration. Using simulated and real datasets, we demonstrate that this approach, called Reference Principal Component Integration (RPCI), consistently outperforms other methods by multiple metrics, with clear advantages in preserving genuine cross-sample gene expression differences in matching cell types, such as those present in cells at distinct developmental stages or in perturbated versus control studies. Moreover, RPCI maintains this robust performance when multiple datasets are integrated. Finally, we applied RPCI to scRNA-seq data for mouse gut endoderm development and revealed temporal emergence of genetic programs helping establish the anterior-posterior axis in visceral endoderm.

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy

During early embryonic development, cell fate commitment represents a critical transition or "tipping point" of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene-gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the "dark genes" that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.

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.

An old weapon with a new function: PIWI-interacting RNAs in neurodegenerative diseases

PIWI-interacting RNAs (piRNAs) are small non-coding transcripts that are highly conserved across species and regulate gene expression through pre- and post-transcriptional processes. piRNAs were originally discovered in germline cells and protect against transposable element expression to promote and maintain genome stability. In the recent decade, emerging roles of piRNAs have been revealed, including the roles in sterility, tumorigenesis, metabolic homeostasis, neurodevelopment, and neurodegenerative diseases. In this review, we summarize piRNA biogenesis in C. elegans, Drosophila, and mice, and further elaborate upon how piRNAs mitigate the harmful effects of transposons. Lastly, the most recent findings on piRNA participation in neurological diseases are highlighted. We speculate on the mechanisms of piRNA action in the development and progression of neurodegenerative diseases. Understanding the roles of piRNAs in neurological diseases may facilitate their applications in diagnostic and therapeutic practice.

c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the c-CSN method, which can construct the conditional cell-specific network (CCSN) for each cell. c-CSN method can measure the direct associations between genes by eliminating the indirect associations. c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less “reliable” gene expression to more “reliable” gene–gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach. 1) One direct association network is generated for one cell. 2) Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices. 3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. c-CSN is publicly available at https://github.com/LinLi-0909/c-CSN.

Overview of PAX gene family: analysis of human tissue-specific variant expression and involvement in human disease

Paired-box (PAX) genes encode a family of highly conserved transcription factors found in vertebrates and invertebrates. PAX proteins are defined by the presence of a paired domain that is evolutionarily conserved across phylogenies. Inclusion of a homeodomain and/or an octapeptide linker subdivides PAX proteins into four groups. Often termed "master regulators", PAX proteins orchestrate tissue and organ development throughout cell differentiation and lineage determination, and are essential for tissue structure and function through maintenance of cell identity. Mutations in PAX genes are associated with myriad human diseases (e.g., microphthalmia, anophthalmia, coloboma, hypothyroidism, acute lymphoblastic leukemia). Transcriptional regulation by PAX proteins is, in part, modulated by expression of alternatively spliced transcripts. Herein, we provide a genomics update on the nine human PAX family members and PAX homologs in 16 additional species. We also present a comprehensive summary of human tissue-specific PAX transcript variant expression and describe potential functional significance of PAX isoforms. While the functional roles of PAX proteins in developmental diseases and cancer are well characterized, much remains to be understood regarding the functional roles of PAX isoforms in human health. We anticipate the analysis of tissue-specific PAX transcript variant expression presented herein can serve as a starting point for such research endeavors.

Defining the Teratoma as a Model for Multi-lineage Human Development

We propose that the teratoma, a recognized standard for validating pluripotency in stem cells, could be a promising platform for studying human developmental processes. Performing single-cell RNA sequencing (RNA-seq) of 179,632 cells across 23 teratomas from 4 cell lines, we found that teratomas reproducibly contain approximately 20 cell types across all 3 germ layers, that inter-teratoma cell type heterogeneity is comparable with organoid systems, and teratoma gut and brain cell types correspond well to similar fetal cell types. Furthermore, cellular barcoding confirmed that injected stem cells robustly engraft and contribute to all lineages. Using pooled CRISPR-Cas9 knockout screens, we showed that teratomas can enable simultaneous assaying of the effects of genetic perturbations across all germ layers. Additionally, we demonstrated that teratomas can be sculpted molecularly via microRNA (miRNA)-regulated suicide gene expression to enrich for specific tissues. Taken together, teratomas are a promising platform for modeling multi-lineage development, pan-tissue functional genetic screening, and tissue engineering.

Single-cell transcriptional diversity is a hallmark of developmental potential

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential-the number of expressed genes per cell-and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.

Technological advances and computational approaches for alternative splicing analysis in single cells

Alternative splicing of RNAs generates isoform diversity, resulting in different proteins that are necessary for maintaining cellular function and identity. The discovery of alternative splicing has been revolutionized by next-generation transcriptomic sequencing mainly using bulk RNA-sequencing, which has unravelled RNA splicing and mis-splicing of normal cells under steady-state and stress conditions. Single-cell RNA-sequencing studies have focused on gene-level expression analysis and revealed gene expression signatures distinguishable between different cellular types. Single-cell alternative splicing is an emerging area of research with the promise to reveal transcriptomic dynamics invisible to bulk- and gene-level analysis. In this review, we will discuss the technological advances for single-cell alternative splicing analysis, computational strategies for isoform detection and quantitation in single cells, and current applications of single-cell alternative splicing analysis and its potential future contributions to personalized medicine.

Spatial genome exploration in the context of cognitive and neurological disease

The 'non-linear' genome, or the spatial proximity of non-contiguous sequences, emerges as an important regulatory layer for genome organization and function, including transcriptional regulation. Here, we review recent genome-scale chromosome conformation mappings ('Hi-C') in developing and adult human and mouse brain. Neural differentiation is associated with widespread remodeling of the chromosomal contact map, reflecting dynamic changes in cell-type-specific gene expression programs, with a massive (estimated 20-50%) net loss of chromosomal contacts that is specific for the neuronal lineage. Hi-C datasets provided an unexpected link between locus-specific abnormal expansion of repeat sequences positioned at the boundaries of self-associating topological chromatin domains, and monogenic neurodevelopmental and neurodegenerative disease. Furthermore, integrative cell-type-specific Hi-C and transcriptomic analysis uncovered an expanded genomic risk space for sequences conferring liability for schizophrenia and other cognitive disease. We predict that spatial genome exploration will deliver radically new insights into the brain nucleome in health and disease.

Forkhead box K2 promotes human colorectal cancer metastasis by upregulating ZEB1 and EGFR

Background: Metastasis is the major reason for high recurrence rates and poor survival among patients with colorectal cancer (CRC). However, the underlying molecular mechanism of CRC metastasis is unclear. This study aimed to investigate the role of forkhead box K2 (FOXK2), one of the most markedly increased FOX genes in CRC, and the mechanism by which it is deregulated in CRC metastasis. Methods: FOXK2 levels were analyzed in two independent human CRC cohorts (cohort I, n = 363; cohort II, n = 390). In vitro Transwell assays and in vivo lung and liver metastasis models were used to examine CRC cell migration, invasion and metastasis. Chromatin immunoprecipitation and luciferase reporter assays were used to measure the binding of transcription factors to the promoters of FOXK2, zinc finger E-box binding homeobox 1 (ZEB1) and epidermal growth factor receptor (EGFR). Cetuximab was utilized to treat FOXK2-mediated metastatic CRC. Results: FOXK2 was significantly upregulated in human CRC tissues, was correlated with more aggressive features and indicated a poor prognosis. FOXK2 overexpression promoted CRC migration, invasion and metastasis, while FOXK2 downregulation had the opposite effects. ZEB1 and EGFR were determined to be direct transcriptional targets of FOXK2 and were essential for FOXK2-mediated CRC metastasis. Moreover, activation of EGFR signaling by EGF enhanced FOXK2 expression via the extracellular regulated protein kinase (ERK) and nuclear factor (NF)-κB pathways. The EGFR monoclonal antibody cetuximab significantly inhibited FOXK2-promoted CRC metastasis. In clinical CRC tissues, FOXK2 expression was positively correlated with the expression of p65, ZEB1 and EGFR. CRC patients who coexpressed p65/FOXK2, FOXK2/ZEB1 and FOXK2/EGFR had poorer prognosis. Conclusions: FOXK2 serves as a prognostic biomarker in CRC. Cetuximab can block the EGF-NF-κB-FOXK2-EGFR feedback loop and suppress CRC metastasis.

Neuron-specific signatures in the chromosomal connectome associated with schizophrenia risk

To explore the developmental reorganization of the three-dimensional genome of the brain in the context of neuropsychiatric disease, we monitored chromosomal conformations in differentiating neural progenitor cells. Neuronal and glial differentiation was associated with widespread developmental remodeling of the chromosomal contact map and included interactions anchored in common variant sequences that confer heritable risk for schizophrenia. We describe cell type-specific chromosomal connectomes composed of schizophrenia risk variants and their distal targets, which altogether show enrichment for genes that regulate neuronal connectivity and chromatin remodeling, and evidence for coordinated transcriptional regulation and proteomic interaction of the participating genes. Developmentally regulated chromosomal conformation changes at schizophrenia-relevant sequences disproportionally occurred in neurons, highlighting the existence of cell type-specific disease risk vulnerabilities in spatial genome organization.

Single-cell RNA-seq reveals dynamic transcriptome profiling in human early neural differentiation

Background

Investigating cell fate decision and subpopulation specification in the context of the neural lineage is fundamental to understanding neurogenesis and neurodegenerative diseases. The differentiation process of neural-tube-like rosettes in vitro is representative of neural tube structures, which are composed of radially organized, columnar epithelial cells and give rise to functional neural cells. However, the underlying regulatory network of cell fate commitment during early neural differentiation remains elusive.

Results

In this study, we investigated the genome-wide transcriptome profile of single cells from six consecutive reprogramming and neural differentiation time points and identified cellular subpopulations present at each differentiation stage. Based on the inferred reconstructed trajectory and the characteristics of subpopulations contributing the most toward commitment to the central nervous system lineage at each stage during differentiation, we identified putative novel transcription factors in regulating neural differentiation. In addition, we dissected the dynamics of chromatin accessibility at the neural differentiation stages and revealed active cis-regulatory elements for transcription factors known to have a key role in neural differentiation as well as for those that we suggest are also involved. Further, communication network analysis demonstrated that cellular interactions most frequently occurred in the embryoid body stage and that each cell subpopulation possessed a distinctive spectrum of ligands and receptors associated with neural differentiation that could reflect the identity of each subpopulation.

Conclusions

Our study provides a comprehensive and integrative study of the transcriptomics and epigenetics of human early neural differentiation, which paves the way for a deeper understanding of the regulatory mechanisms driving the differentiation of the neural lineage.

Quantifying Waddington's epigenetic landscape: a comparison of single-cell potency measures

MOTIVATION

Estimating differentiation potency of single cells is a task of great biological and clinical significance, as it may allow identification of normal and cancer stem cell phenotypes. However, very few single-cell potency models have been proposed, and their robustness and reliability across independent studies have not yet been fully assessed.

RESULTS

Using nine independent single-cell RNA-Seq experiments, we here compare four different single-cell potency models to each other, in their ability to discriminate cells that ought to differ in terms of differentiation potency. Two of the potency models approximate potency via network entropy measures that integrate the single-cell RNA-Seq profile of a cell with a protein interaction network. The comparison between the four models reveals that integration of RNA-Seq data with a protein interaction network dramatically improves the robustness and reliability of single-cell potency estimates. We demonstrate that underlying this robustness is a correlation relationship, according to which high differentiation potency is positively associated with overexpression of network hubs. We further show that overexpressed network hubs are strongly enriched for ribosomal mitochondrial proteins, suggesting that their mRNA levels may provide a universal marker of a cell's potency. Thus, this study provides novel systems-biological insight into cellular potency and may provide a foundation for improved models of differentiation potency with far-reaching implications for the discovery of novel stem cell or progenitor cell phenotypes.

Understanding the Biology and Pathogenesis of the Kidney by Single-Cell Transcriptomic Analysis

Background

Single-cell RNA-seq (scRNA-seq) has recently emerged as a revolutionary and powerful tool for biomedical research. However, there have been relatively few studies using scRNA-seq in the field of kidney study.

Summary

scRNA-seq achieves gene expression profiling at single-cell resolution in contrast with the conventional methods of gene expression profiling, which are based on cell population and give averaged values of gene expression of the cells. Single-cell transcriptomic analysis is crucial because individual cells of the same type are highly heterogeneous in gene expression, which reflects the existence of subpopulations, different cellular states, or molecular dynamics, of the cells, and should be resolved for further insights. In addition, gene expression analysis of tissues or organs that usually comprise multiple cell types or subtypes results in data that are not fully applicable to any given cell type. scRNA-seq is capable of identifying all cell types and subtypes in a tissue, including those that are new or present in small quantity. With these unique capabilities, scRNA-seq has been used to dissect molecular processes in cell differentiation and to trace cell lineages in development. It is also used to analyze the cells in a lesion of disease to identify the cell types and molecular dynamics implicated in the injury. With continuous technical improvement, scRNA-seq has become extremely high throughput and cost effective, making it accessible to all laboratories. In the present review article, we provide an overall review of scRNA-seq concerning its history, improvements, and applications. In addition, we describe the available studies in which scRNA-seq was employed in the field of kidney research. Lastly, we discuss other potential uses of scRNA-seq for kidney research.

Key Message

This review article provides general information on scRNA-seq and its various uses. Particularly, we summarize the studies in the field of kidney diseases in which scRNA-seq was used and discuss potential additional uses of scRNA-seq for kidney research.