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Connecting past and present: single-cell lineage tracing.
Central to the core principle of cell theory, depicting cells' history, state and fate is a fundamental goal in modern biology. By leveraging clonal analysis and single-cell RNA-seq technologies, single-cell lineage tracing provides new opportunities to interrogate both cell states and lineage histories. During the past few years, many strategies to achieve lineage tracing at single-cell resolution have been developed, and three of them (integration barcodes, polylox barcodes, and CRISPR barcodes) are noteworthy as they are amenable in experimentally tractable systems. Although the above strategies have been demonstrated in animal development and stem cell research, much care and effort are still required to implement these methods. Here we review the development of single-cell lineage tracing, major characteristics of the cell barcoding strategies, applications, as well as technical considerations and limitations, providing a guide to choose or improve the single-cell barcoding lineage tracing.
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The coding and long noncoding single-cell atlas of the developing human fetal striatum.
Deciphering how the human striatum develops is necessary for understanding the diseases that affect this region. To decode the transcriptional modules that regulate this structure during development, we compiled a catalog of 1116 long intergenic noncoding RNAs (lincRNAs) identified de novo and then profiled 96,789 single cells from the early human fetal striatum. We found that D1 and D2 medium spiny neurons (D1- and D2-MSNs) arise from a common progenitor and that lineage commitment is established during the postmitotic transition, across a pre-MSN phase that exhibits a continuous spectrum of fate determinants. We then uncovered cell type-specific gene regulatory networks that we validated through in silico perturbation. Finally, we identified human-specific lincRNAs that contribute to the phylogenetic divergence of this structure in humans. This work delineates the cellular hierarchies governing MSN lineage commitment.
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Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH.
A mammalian brain is composed of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing allows systematic identification of cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain1,2. Single-cell epigenomic profiling3,4 further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation. Here we used a single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)5, to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. We profiled approximately 300,000 cells in the mouse primary motor cortex and its adjacent areas, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory but also most inhibitory neuronal clusters adopted laminar organizations. Intratelencephalic neurons formed a largely continuous gradient along the cortical depth axis, in which the gene expression of individual cells correlated with their cortical depths. Furthermore, we integrated MERFISH with retrograde labelling to probe projection targets of neurons of the mouse primary motor cortex and found that their cortical projections formed a complex network in which individual neuronal clusters project to multiple target regions and individual target regions receive inputs from multiple neuronal clusters.
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A prognostic risk prediction model for gastric cancer based on the EFNA4 and ETS1 regulatory axis in tumor cells.
Gastric cancer (GC) is a major cause of cancer-related deaths worldwide, and is characterised by intricate molecular mechanisms. However, analysis of its molecular and clinical characteristics is complicated by its histological and etiological heterogeneity. Dysregulation of the PI3K-Akt signalling pathway is common in GC. In this study, we have identified the hub gene Ephrin A4 (EFNA4) in the PI3K-Akt pathway based on transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and ETS Proto-Oncogene 1 (ETS1) was a gene related to EFNA4. Using publicly accessible datasets, we conducted bioinformatics analyses to evaluate the expression profiles, functional roles, and prognostic significance of EFNA4 and ETS1 and further explored their relationship in GC. Subsequently, consensus clustering was performed on 373 TCGA-STAD datasets based on the expression matrices of EFNA4 and ETS1 to assess their interconnections with relevant signalling cascades and immune system components. To address the challenges posed by tumour heterogeneity and reveal the expression patterns of EFNA4 and ETS1 in GC tissues, we performed reanalysis of single-cell RNA sequencing (scRNA-seq) data of GC samples. We constructed a tumour-based risk signature for GC based on EFNA4, ETS1, and marker genes of the tumour cell cluster. The prognostic value of the prognosis prediction model was verified using TCGA database to facilitate the clinical application of tumour cell features in GC prognosis. Our study reveals EFNA4 and ETS1 expression patterns in GC, implicating their roles in pathogenesis. An integrated EFNA4-ETS1 prognostic model improves GC risk stratification. Although EFNA4 has been shown to promote metastasis in liver cancer, the contradictory mechanism of its high expression and good prognosis in GC remains to be elucidated, which may involve its antagonistic effects with ETS1, and requires further exploration.
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Clustering of Omic Data Using Semi-Supervised Transfer Learning for Gaussian Mixture Models via Natural-Gradient Variational Inference: Method and Applications to Bulk and Single-Cell Transcriptomics.
Recent advances in high-throughput technologies have enabled observational studies to collect high-dimensional omic data. However, such data, often measured on small sample sizes, pose challenges to model-based clustering approaches such as Gaussian Mixture Models. Existing methods often fail to generalize due to model instability under complex mixture patterns. To overcome these limitations, we propose a natural-gradient variational inference framework for Gaussian mixture models named Praxis-BGM that incorporates informative priors-cluster-specific means, covariances, and structural connectivity-from large-scale reference data with known cluster or class labels to enable semi-supervised transfer learning. We derive natural-gradient updates that integrate prior knowledge, leveraging the Variational Online Newton algorithm. We also perform feature selection for clustering using Bayes Factors. Implemented using the JAX library for accelerator-oriented computation, Praxis-BGM is computationally efficient and scalable. We demonstrate the effectiveness of Praxis-BGM in extensive simulations and with two real-world applications: bulk transcriptomic datasets for breast cancer subtyping (the Cancer Genome Atlas Breast Invasive Carcinoma and the Molecular Taxonomy of Breast Cancer International Consortium), and transferring cell-type annotations between single-cell transcriptomic datasets produced by different single-cell RNA-seq technologies in a human pancreas study. Even when priors are partially mismatched with the target data, Praxis-BGM enhances semi-supervised clustering accuracy and biological interpretability.
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A cell-type-specific atlas of the inner ear transcriptional response to acoustic trauma.
Noise-induced hearing loss (NIHL) results from a complex interplay of damage to the sensory cells of the inner ear, dysfunction of its lateral wall, axonal retraction of type 1C spiral ganglion neurons, and activation of the immune response. We use RiboTag and single-cell RNA sequencing to survey the cell-type-specific molecular landscape of the mouse inner ear before and after noise trauma. We identify induction of the transcription factors STAT3 and IRF7 and immune-related genes across all cell-types. Yet, cell-type-specific transcriptomic changes dominate the response. The ATF3/ATF4 stress-response pathway is robustly induced in the type 1A noise-resilient neurons, potassium transport genes are downregulated in the lateral wall, mRNA metabolism genes are downregulated in outer hair cells, and deafness-associated genes are downregulated in most cell types. This transcriptomic resource is available via the Gene Expression Analysis Resource (gEAR; https://umgear.org/NIHL) and provides a blueprint for the rational development of drugs to prevent and treat NIHL.
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Deep tissue profiling of Populus stem at single nucleus level reveals uncharacterized cell types and cell-specific gene regulatory networks.
BACKGROUND: Single-cell genomics is revolutionizing plant developmental biology, enabling the transcriptome profiling of individual cells and their lineage relationships. However, plant cell walls polymers hamper the dissociation and analysis of intact cells. This rigid structure can conceal cell types embedded in complex, lignified, multi-cell layered tissues such as those undergoing secondary growth. Their absence leads to incomplete single-cell genomic atlases and lineage inferences. RESULTS: We isolate nuclei to capture transcripts representing the diversity of cells throughout the stem of the woody perennial Populus trichocarpa generating a high-resolution transcriptome atlas of cell types and lineage trajectories. RNA sequencing of 11,673 nuclei identifies 26 clusters representing cell types in the cambium, xylem, phloem, and periderm. Comparative analysis with protoplast-derived transcriptome data reveals significant biases, with nuclei-based sequencing providing a higher representation of cells in lignified inner xylem tissues. Among previously underrepresented types, we uncover vessel-associated cells (VAC), a largely uncharacterized parenchyma subtype and the terminus of a xylem cell lineage. Gene regulatory analysis identifies a VAC-specific network and the Populus MYB48 as its primary regulator. Functional validation of MYB48 knockout mutants show an increase in vessel number and size, pointing to a role of VACs in vessel development. CONCLUSIONS: Our study demonstrates the capture and transcriptome characterization of cell types embedded in plant secondary growth, identifying novel regulators of xylem development and stress adaptation. The discovery of MYB48 as a key regulator of VAC function highlights a previously uncharacterized mechanism influencing vessel development, with applications to improving wood formation and stress resilience.
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Single-Cell Transcriptomic Atlas of Primate Ovarian Aging.
Molecular mechanisms of ovarian aging and female age-related fertility decline remain unclear. We surveyed the single-cell transcriptomic landscape of ovaries from young and aged non-human primates (NHPs) and identified seven ovarian cell types with distinct gene-expression signatures, including oocyte and six types of ovarian somatic cells. In-depth dissection of gene-expression dynamics of oocytes revealed four subtypes at sequential and stepwise developmental stages. Further analysis of cell-type-specific aging-associated transcriptional changes uncovered the disturbance of antioxidant signaling specific to early-stage oocytes and granulosa cells, indicative of oxidative damage as a crucial factor in ovarian functional decline with age. Additionally, inactivated antioxidative pathways, increased reactive oxygen species, and apoptosis were observed in granulosa cells from aged women. This study provides a comprehensive understanding of the cell-type-specific mechanisms underlying primate ovarian aging at single-cell resolution, revealing new diagnostic biomarkers and potential therapeutic targets for age-related human ovarian disorders.
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Comparative single-cell transcriptomic atlases of drosophilid brains suggest glial evolution during ecological adaptation.
To explore how brains change upon species evolution, we generated single-cell transcriptomic atlases of the central brains of three closely related but ecologically distinct drosophilids: the generalists Drosophila melanogaster and Drosophila simulans, and the noni fruit specialist Drosophila sechellia. The global cellular composition of these species' brains is well-conserved, but we predicted a few cell types with different frequencies, notably perineurial glia of the blood-brain barrier, which we validate in vivo. Gene expression analysis revealed that distinct cell types evolve at different rates and patterns, with glial populations exhibiting the greatest divergence between species. Compared to the D. melanogaster brain, cellular composition and gene expression patterns are more divergent in D. sechellia than in D. simulans-despite their similar phylogenetic distance from D. melanogaster-indicating that the specialization of D. sechellia is reflected in the structure and function of its brain. Expression changes in D. sechellia include several metabolic signaling genes, suggestive of adaptations to its novel source of nutrition. Additional single-cell transcriptomic analysis on D. sechellia revealed genes and cell types responsive to dietary supplement with noni, pointing to glia as sites for both physiological and genetic adaptation to this fruit. Our atlases represent the first comparative datasets for "whole" central brains and provide a comprehensive foundation for studying the evolvability of nervous systems in a well-defined phylogenetic and ecological framework.
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A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility.
We applied a combinatorial indexing assay, sci-ATAC-seq, to profile genome-wide chromatin accessibility in ∼100,000 single cells from 13 adult mouse tissues. We identify 85 distinct patterns of chromatin accessibility, most of which can be assigned to cell types, and ∼400,000 differentially accessible elements. We use these data to link regulatory elements to their target genes, to define the transcription factor grammar specifying each cell type, and to discover in vivo correlates of heterogeneity in accessibility within cell types. We develop a technique for mapping single cell gene expression data to single-cell chromatin accessibility data, facilitating the comparison of atlases. By intersecting mouse chromatin accessibility with human genome-wide association summary statistics, we identify cell-type-specific enrichments of the heritability signal for hundreds of complex traits. These data define the in vivo landscape of the regulatory genome for common mammalian cell types at single-cell resolution.