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Spatial dissimilarity analysis in single-cell transcriptomics.
We develop the spatial dissimilarity method to uncover complex bivariate relationships in single-cell and spatial transcriptomics data, addressing challenges such as alternative splicing and allele-specific gene expression. Applying this method to detect alternative splicing in neurons demonstrates improved accuracy and sensitivity compared to existing tools, notably identifying neuron subtypes. In tumor cells, spatial dissimilarity analysis reveals somatic variants that emerge during tumor progression, validated through whole-exome sequencing. These findings highlight how allele-specific genetic variants contribute to the subclone architecture of cancer cells, offering insights into cellular heterogeneity. Applied on a human cell atlas, we uncover numerous cases of allele-specific expression of genes in normal cells. We provide a software package for spatial dissimilarity analysis to enable enhanced understanding of cellular complexity and gene expression dynamics under homeostatic conditions and during states of transitions.
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A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain.
The diversity of cell types and regulatory states in the brain, and how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas of the entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial cell clusters that are further subclustered and validated by targeted cell-sorting. Our data show high granularity and identify a wide range of cell types. Gene network analyses using SCENIC revealed regulatory heterogeneity linked to energy consumption. During aging, RNA content declines exponentially without affecting neuronal identity in old brains. This single-cell brain atlas covers nearly all cells in the normal brain and provides the tools to study cellular diversity alongside other Drosophila and mammalian single-cell datasets in our unique single-cell analysis platform: SCope (http://scope.aertslab.org). These results, together with SCope, allow comprehensive exploration of all transcriptional states of an entire aging brain.
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Why "Where" Matters as Much as "How Much": Single-Cell and Spatial Transcriptomics in Plants.
Plant tissues exhibit a layered architecture that makes spatial context decisive for interpreting transcriptional changes. This review explains why the location of gene expression is as important as its magnitude and synthesizes advances uniting single-cell/nucleus RNA-seq with spatial transcriptomics in plants. Surveyed topics include platform selection and material preparation; plant-specific sample processing and quality control; integration with epigenomic assays such as single-nucleus Assay for Transposase-Accessible Chromatin using sequencing (ATAC) and Multiome; and computational workflows for label transfer, deconvolution, spatial embedding, and neighborhood-aware cell-cell communication. Protoplast-based single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling but introduces dissociation artifacts and cell-type biases, whereas ingle-nucleus RNA sequencing (snRNA-seq) improves the representation of recalcitrant lineages and reduces stress signatures while remaining compatible with multiomics profiling. Practical guidance is provided for mitigating ambient RNA, interpreting organellar and intronic metrics, identifying doublets, and harmonizing batches across chemistries and studies. Spatial platforms (Visium HD, Stereo-seq, bead arrays) and targeted imaging (Single-molecule fluorescence in situ hybridization (smFISH), Hairpin-chain-reaction FISH (HCR-FISH), Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH)) are contrasted with plant-specific adaptations and integration pipelines that anchor dissociated profiles in anatomical coordinates. Recent atlases in Arabidopsis, soybean, and maize illustrate how cell identities, chromatin accessibility, and spatial niches reveal developmental trajectories and stress responses jointly. A roadmap is outlined for moving from atlases to interventions by deriving gene regulatory networks, prioritizing cis-regulatory targets, and validating perturbations with spatial readouts in crops. Together, these principles support a transition from descriptive maps to mechanism-informed, low-pleiotropy engineering of agronomic traits.
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A single-cell atlas of the murine limb skeleton integrating the developmental and adult stages.
The recent growth of single-cell transcriptomics has made single-cell RNA sequencing (scRNA-seq) into a near-routine technique. Breakthroughs in scalability have led to the creation of organism-wide transcriptomic datasets, aiming to comprehensively profile the cell types and states within an organism throughout its lifecycle. However, the skeleton remains an underrepresented organ system in organism-wide atlases. Given the skeleton's critical role as the central framework of the vertebrate body, its function in housing the hematopoietic niche, and its involvement in metabolic and homeostatic processes, its underrepresentation presents a significant gap in current reference atlas projects. To address this issue, we integrated ten separate murine, publicly available scRNA-seq datasets, which include limb skeletal cells and their developmental precursors, resulting in an atlas of 133,332 cells. This limb skeletal cell atlas describes cells within the mesenchymal lineage, focusing on the process from limb induction to adult bone formation, and encompasses 39 well-characterized cell types and states. By expanding the repertoire of time points and cell types within a single dataset, we enable more complete analyses of cell-cell communication or in silico perturbation studies. Together, these efforts present a valuable resource for researchers in skeletal biology, metabolism, and regenerative medicine, filling an important gap in current atlas mapping projects.
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Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads.
Single-cell RNA-seq and single-cell assay for transposase-accessible chromatin (ATAC-seq) technologies are used extensively to create cell type atlases for a wide range of organisms, tissues, and disease processes. To increase the scale of these atlases, lower the cost and pave the way for more specialized multiome assays, custom droplet microfluidics may provide solutions complementary to commercial setups. We developed HyDrop, a flexible and open-source droplet microfluidic platform encompassing three protocols. The first protocol involves creating dissolvable hydrogel beads with custom oligos that can be released in the droplets. In the second protocol, we demonstrate the use of these beads for HyDrop-ATAC, a low-cost noncommercial scATAC-seq protocol in droplets. After validating HyDrop-ATAC, we applied it to flash-frozen mouse cortex and generated 7996 high-quality single-cell chromatin accessibility profiles in a single run. In the third protocol, we adapt both the reaction chemistry and the capture sequence of the barcoded hydrogel bead to capture mRNA, and demonstrate a significant improvement in throughput and sensitivity compared to previous open-source droplet-based scRNA-seq assays (Drop-seq and inDrop). Similarly, we applied HyDrop-RNA to flash-frozen mouse cortex and generated 9508 single-cell transcriptomes closely matching reference single-cell gene expression data. Finally, we leveraged HyDrop-RNA's high capture rate to analyze a small population of fluorescence-activated cell sorted neurons from the Drosophila brain, confirming the protocol's applicability to low input samples and small cells. HyDrop is currently capable of generating single-cell data in high throughput and at a reduced cost compared to commercial methods, and we envision that HyDrop can be further developed to be compatible with novel (multi) omics protocols.
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Single-cell profiling for advancing birth defects research and prevention.
Cellular analysis of developmental processes and toxicities has traditionally entailed bulk methods (e.g., transcriptomics) that lack single cell resolution or tissue localization methods (e.g., immunostaining) that allow only a few genes to be monitored in each experiment. Recent technological advances have enabled interrogation of genomic function at the single-cell level, providing new opportunities to unravel developmental pathways and processes with unprecedented resolution. Here, we review emerging technologies of single-cell RNA-sequencing (scRNA-seq) to globally characterize the gene expression sets of different cell types and how different cell types emerge from earlier cell states in development. Cell atlases of experimental embryology and human embryogenesis at single-cell resolution will provide an encyclopedia of genes that define key stages from gastrulation to organogenesis. This technology, combined with computational models to discover key organizational principles, was recognized by Science magazine as the "Breakthrough of the year" for 2018 due to transformative potential on the way we study how human cells mature over a lifetime, how tissues regenerate, and how cells change in diseases (e.g., patient-derived organoids to screen disease-specific targets and design precision therapy). Profiling transcriptomes at the single-cell level can fulfill the need for greater detail in the molecular progression of all cell lineages, from pluripotency to adulthood and how cell-cell signaling pathways control progression at every step. Translational opportunities emerge for elucidating pathogenesis of genetic birth defects with cellular precision and improvements for predictive toxicology of chemical teratogenesis.
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Defining the Transcriptional Landscape during Cytomegalovirus Latency with Single-Cell RNA Sequencing.
Primary infection with human cytomegalovirus (HCMV) results in a lifelong infection due to its ability to establish latent infection, with one characterized viral reservoir being hematopoietic cells. Although reactivation from latency causes serious disease in immunocompromised individuals, our molecular understanding of latency is limited. Here, we delineate viral gene expression during natural HCMV persistent infection by analyzing the massive transcriptome RNA sequencing (RNA-seq) atlas generated by the Genotype-Tissue Expression (GTEx) project. This systematic analysis reveals that HCMV persistence in vivo is prevalent in diverse tissues. Notably, we find only viral transcripts that resemble gene expression during various stages of lytic infection with no evidence of any highly restricted latency-associated viral gene expression program. To further define the transcriptional landscape during HCMV latent infection, we also used single-cell RNA-seq and a tractable experimental latency model. In contrast to some current views on latency, we also find no evidence for any highly restricted latency-associated viral gene expression program. Instead, we reveal that latency-associated gene expression largely mirrors a late lytic viral program, albeit at much lower levels of expression. Overall, our work has the potential to revolutionize our understanding of HCMV persistence and suggests that latency is governed mainly by quantitative changes, with a limited number of qualitative changes, in viral gene expression.IMPORTANCE Human cytomegalovirus is a prevalent pathogen, infecting most of the population worldwide and establishing lifelong latency in its hosts. Although reactivation from latency causes significant morbidity and mortality in immunocompromised hosts, our molecular understanding of the latent state remains limited. Here, we examine the viral gene expression during natural and experimental latent HCMV infection on a transcriptome-wide level. In contrast to the classical views on herpesvirus latency, we find no evidence for a restricted latency-associated viral gene expression program. Instead, we reveal that latency gene expression largely resembles a late lytic viral profile, albeit at much lower levels of expression. Taken together, our data transform the current view of HCMV persistence and suggest that latency is mainly governed by quantitative rather than qualitative changes in viral gene expression.
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Acquisition, processing, and single-cell analysis of normal human breast tissues from a biobank.
The Komen Tissue Bank is the only biorepository in the world for normal breast tissues from women. Below we report the acquisition and processing of breast tissue from volunteer donors and describe an experimental and analysis pipeline to generate a single-cell atlas. This atlas is based on single-cell RNA-seq and is useful to derive breast epithelial cell subcluster-specific gene expression signatures, which can be applied to breast cancer gene expression data to identify putative cell-of-origin. For complete details on the use and execution of this protocol, please refer to Bhat-Nakshatri et al. (2021).
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Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas.
Recent progress in single-cell technologies has enabled the identification of all major cell types in mouse. However, for most cell types, the regulatory mechanism underlying their identity remains poorly understood. By computational analysis of the recently published mouse cell atlas data, we have identified 202 regulons whose activities are highly variable across different cell types, and more importantly, predicted a small set of essential regulators for each major cell type in mouse. Systematic validation by automated literature and data mining provides strong additional support for our predictions. Thus, these predictions serve as a valuable resource that would be useful for the broad biological community. Finally, we have built a user-friendly, interactive web portal to enable users to navigate this mouse cell network atlas.
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scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data.
Single-cell RNA sequencing (scRNA-seq) allows quantitative analysis of gene expression at the level of single cells, beneficial to study cell heterogeneity. The recognition of cell types facilitates the construction of cell atlas in complex tissues or organisms, which is the basis of almost all downstream scRNA-seq data analyses. Using disease-related scRNA-seq data to perform the prediction of disease status can facilitate the specific diagnosis and personalized treatment of disease. Since single-cell gene expression data are high-dimensional and sparse with dropouts, we propose scIAE, an integrative autoencoder-based ensemble classification framework, to firstly perform multiple random projections and apply integrative and devisable autoencoders (integrating stacked, denoising and sparse autoencoders) to obtain compressed representations. Then base classifiers are built on the lower-dimensional representations and the predictions from all base models are integrated. The comparison of scIAE and common feature extraction methods shows that scIAE is effective and robust, independent of the choice of dimension, which is beneficial to subsequent cell classification. By testing scIAE on different types of data and comparing it with existing general and single-cell-specific classification methods, it is proven that scIAE has a great classification power in cell type annotation intradataset, across batches, across platforms and across species, and also disease status prediction. The architecture of scIAE is flexible and devisable, and it is available at https://github.com/JGuan-lab/scIAE.