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Neural crest cells exemplify cellular diversification from a multipotent progenitor population. However, the full sequence of early molecular choices orchestrating the emergence of neural crest heterogeneity from the embryonic ectoderm remains elusive. Gene-regulatory-networks (GRN) govern early development and cell specification toward definitive neural crest. Here, we combine ultradense single-cell transcriptomes with machine-learning and large-scale transcriptomic and epigenomic experimental validation of selected trajectories, to provide the general principles and highlight specific features of the GRN underlying neural crest fate diversification from induction to early migration stages using Xenopus frog embryos as a model. During gastrulation, a transient neural border zone state precedes the choice between neural crest and placodes which includes multiple converging gene programs. During neurulation, transcription factor connectome, and bifurcation analyses demonstrate the early emergence of neural crest fates at the neural plate stage, alongside an unbiased multipotent-like lineage persisting until epithelial-mesenchymal transition stage. We also decipher circuits driving cranial and vagal neural crest formation and provide a broadly applicable high-throughput validation strategy for investigating single-cell transcriptomes in vertebrate GRNs in development, evolution, and disease.

Abstract Background Many laboratories have described the in vitro isolation of multipotent cells with stem cell properties from the skin of various species termed skin-derived stem cells (SDSCs). However, the cellular origin of these cells and their capability to give rise, among various cell types, to male germ cells, remain largely unexplored. Methods SDSCs were isolated from newborn mice skin, and then differentiated into primordial germ cell-like cells (PGCLCs) in vitro. Single-cell RNA sequencing (scRNA-seq) was then applied to dissect the cellular origin of SDSCs using cells isolated from newborn mouse skin and SDSC colonies. Based on an optimized culture strategy, we successfully generated spermatogonial stem cell-like cells (SSCLCs) in vitro. Results Here, using scRNA-seq and analyzing the profile of 7543 single-cell transcriptomes from newborn mouse skin and SDSCs, we discovered that they mainly consist of multipotent papillary dermal fibroblast progenitors (pDFPs) residing in the dermal layer. Moreover, we found that epidermal growth factor (EGF) signaling is pivotal for the capability of these progenitors to proliferate and form large colonies in vitro. Finally, we optimized the protocol to efficiently generate PGCLCs from SDSCs. Furthermore, PGCLCs were induced into SSCLCs and these SSCLCs showed meiotic potential when cultured with testicular organoids. Conclusions Our findings here identify pDFPs as SDSCs derived from newborn skin and show for the first time that such precursors can be induced to generate cells of the male germline.

We propose PreCanCell, a novel algorithm for predicting malignant and non-malignant cells from single-cell transcriptomes. PreCanCell first identifies the differentially expressed genes (DEGs) between malignant and non-malignant cells commonly in five common cancer types-associated single-cell transcriptome datasets. The five common cancer types include renal cell carcinoma (RCC), head and neck squamous cell carcinoma (HNSCC), melanoma, lung adenocarcinoma (LUAD), and breast cancer (BC). With each of the five datasets as the training set and the DEGs as the features, a single cell is classified as malignant or non-malignant by k-NN (k = 5). Finally, the single cell is determined as malignant or non-malignant by the majority vote of the five k-NN classification results. We tested the predictive performance of PreCanCell in 19 single-cell datasets, and reported classification accuracy, sensitivity, specificity, balanced accuracy (the average of sensitivity and specificity) and the area under the receiver operating characteristic curve (AUROC). In all these datasets, PreCanCell achieved above 0.8 accuracy, sensitivity, specificity, balanced accuracy and AUROC. Finally, we compared the predictive performance of PreCanCell with that of seven other algorithms, including CHETAH, SciBet, SCINA, scmap-cell, scmap-cluster, SingleR, and ikarus. Compared to these algorithms, PreCanCell displays the advantages of higher accuracy and simpler implementation. We have developed an R package for the PreCanCell algorithm, which is available at https://github.com/WangX-Lab/PreCanCell.

According to estimations, approximately about 15% of couples worldwide suffer from infertility, in which individuals with azoospermia or oocyte abnormalities cannot be treated with assisted reproductive technology. The skin-derived stem cells (SDSCs) differentiation into primordial germ cell-like cells (PGCLCs) is one of the major breakthroughs in the field of stem cells intervention for infertility treatment in recent years. However, the cellular origin of SDSCs and their dynamic changes in transcription profile during differentiation into PGCLCs in vitro remain largely undissected. Here, the results of single-cell RNA sequencing indicated that porcine SDSCs are mainly derived from multipotent dermal fibroblast progenitors (MDFPs), which are regulated by growth factors (EGF/bFGF). Importantly, porcine SDSCs exhibit pluripotency for differentiating into three germ layers and can effectively differentiate into PGCLCs through complex transcriptional regulation involving histone modification. Moreover, this study also highlights that porcine SDSC-derived PGCLCs specification exhibit conservation with the human primordial germ cells lineage and that its proliferation is mediated by the MAPK signaling pathway. Our findings provide substantial novel insights into the field of regenerative medicine in which stem cells differentiate into germ cells in vitro, as well as potential therapeutic effects in individuals with azoospermia and/or defective oocytes. [ABSTRACT FROM AUTHOR]

Background: Many laboratories have described the in vitro isolation of multipotent cells with stem cell properties from the skin of various species termed skin-derived stem cells (SDSCs). However, the cellular origin of these cells and their capability to give rise, among various cell types, to male germ cells, remain largely unexplored. Methods: SDSCs were isolated from newborn mice skin, and then differentiated into primordial germ cell-like cells (PGCLCs) in vitro. Single-cell RNA sequencing (scRNA-seq) was then applied to dissect the cellular origin of SDSCs using cells isolated from newborn mouse skin and SDSC colonies. Based on an optimized culture strategy, we successfully generated spermatogonial stem cell-like cells (SSCLCs) in vitro. Results: Here, using scRNA-seq and analyzing the profile of 7543 single-cell transcriptomes from newborn mouse skin and SDSCs, we discovered that they mainly consist of multipotent papillary dermal fibroblast progenitors (pDFPs) residing in the dermal layer. Moreover, we found that epidermal growth factor (EGF) signaling is pivotal for the capability of these progenitors to proliferate and form large colonies in vitro. Finally, we optimized the protocol to efficiently generate PGCLCs from SDSCs. Furthermore, PGCLCs were induced into SSCLCs and these SSCLCs showed meiotic potential when cultured with testicular organoids. Conclusions: Our findings here identify pDFPs as SDSCs derived from newborn skin and show for the first time that such precursors can be induced to generate cells of the male germline. [ABSTRACT FROM AUTHOR]

Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulations of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 11.9 million single cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene–gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside. [ABSTRACT FROM AUTHOR]

Abstract Background The human small airway epithelium (SAE) plays a central role in the early events in the pathogenesis of most inherited and acquired lung disorders. Little is known about the molecular phenotypes of the specific cell populations comprising the SAE in humans, and the contribution of SAE specific cell populations to the risk for lung diseases. Methods Drop-seq single-cell RNA-sequencing was used to characterize the transcriptome of single cells from human SAE of nonsmokers and smokers by bronchoscopic brushing. Results Eleven distinct cell populations were identified, including major and rare epithelial cells, and immune/inflammatory cells. There was cell type-specific expression of genes relevant to the risk of the inherited pulmonary disorders, genes associated with risk of chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis and (non-mutated) driver genes for lung cancers. Cigarette smoking significantly altered the cell type-specific transcriptomes and disease risk-related genes. Conclusions This data provides new insights into the possible contribution of specific lung cells to the pathogenesis of lung disorders.

Background: The human small airway epithelium (SAE) plays a central role in the early events in the pathogenesis of most inherited and acquired lung disorders. Little is known about the molecular phenotypes of the specific cell populations comprising the SAE in humans, and the contribution of SAE specific cell populations to the risk for lung diseases.Methods: Drop-seq single-cell RNA-sequencing was used to characterize the transcriptome of single cells from human SAE of nonsmokers and smokers by bronchoscopic brushing.Results: Eleven distinct cell populations were identified, including major and rare epithelial cells, and immune/inflammatory cells. There was cell type-specific expression of genes relevant to the risk of the inherited pulmonary disorders, genes associated with risk of chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis and (non-mutated) driver genes for lung cancers. Cigarette smoking significantly altered the cell type-specific transcriptomes and disease risk-related genes.Conclusions: This data provides new insights into the possible contribution of specific lung cells to the pathogenesis of lung disorders. [ABSTRACT FROM AUTHOR]

Keywords: emerging model systems; live imaging; modelling; plant development; single-cell transcriptomes Analyses of single-cell RNA-seq data have allowed the Efroni laboratory to identify populations of cells with unique transcriptomic signatures in aerial root primordia that correspond to different domains of conventional roots. Bert De Rybel (VIB/Ghent University, Belgium) described his use of single-cell RNA sequencing to map cell lineages in the I Arabidopsis i root, with the goal of calculating the developmental trajectories of cells within different tissue types. CRISPR-induced mutations to cyclin-dependent kinases and auxin-response factors specifically within lateral roots have led to insights about the relationship between lateral and primary roots, and the sequence of expression of key lateral root regulators. [Extracted from the article]

Ischemic brain injury is a severe medical condition with high incidences in elderly people without effective treatment for the resulting neural damages. Using a unilateral mouse stroke model, we analyze single-cell transcriptomes of ipsilateral and contralateral cortical penumbra regions to objectively reveal molecular events with single-cell resolution at 4 h and 1, 3, and 7 days post-injury. Here, we report that neurons are among the first cells that sense the lack of blood supplies by elevated expression of CCAAT/enhancer-binding protein β (C/EBPβ). To our surprise, the canonical inflammatory cytokine gene targets for C/EBPβ, including interleukin-1β (IL-1β) and tumor necrosis factor α (TNF-α), are subsequently induced also in neuronal cells. Neuronal-specific silencing of C/EBPβ or IL-1β and TNF-α substantially alleviates downstream inflammatory injury responses and is profoundly neural protective. Taken together, our findings reveal a neuronal inflammatory mechanism underlying early pathological triggers of ischemic brain injury., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

The challenges in recapitulating in vivo human T cell development in laboratory models have posed a barrier to understanding human thymopoiesis. Here, we used single-cell RNA sequencing (sRNA-seq) to interrogate the rare CD34 + progenitor and the more differentiated CD34 - fractions in the human postnatal thymus. CD34 + thymic progenitors were comprised of a spectrum of specification and commitment states characterized by multilineage priming followed by gradual T cell commitment. The earliest progenitors in the differentiation trajectory were CD7 - and expressed a stem-cell-like transcriptional profile, but had also initiated T cell priming. Clustering analysis identified a CD34 + subpopulation primed for the plasmacytoid dendritic lineage, suggesting an intrathymic dendritic specification pathway. CD2 expression defined T cell commitment stages where loss of B cell potential preceded that of myeloid potential. These datasets delineate gene expression profiles spanning key differentiation events in human thymopoiesis and provide a resource for the further study of human T cell development., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Summary Understanding the organizational logic of neural circuits requires deciphering the biological basis of neuronal diversity and identity, but there is no consensus on how neuron types should be defined. We analyzed single-cell transcriptomes of a set of anatomically and physiologically characterized cortical GABAergic neurons and conducted a computational genomic screen for transcriptional profiles that distinguish them from one another. We discovered that cardinal GABAergic neuron types are delineated by a transcriptional architecture that encodes their synaptic communication patterns. This architecture comprises 6 categories of ∼40 gene families, including cell-adhesion molecules, transmitter-modulator receptors, ion channels, signaling proteins, neuropeptides and vesicular release components, and transcription factors. Combinatorial expression of select members across families shapes a multi-layered molecular scaffold along the cell membrane that may customize synaptic connectivity patterns and input-output signaling properties. This molecular genetic framework of neuronal identity integrates cell phenotypes along multiple axes and provides a foundation for discovering and classifying neuron types. [ABSTRACT FROM AUTHOR]