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Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data.
Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways' activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualize new single-cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.
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A multi-omics atlas of the human retina at single-cell resolution.
Cell classes in the human retina are highly heterogeneous with their abundance varying by several orders of magnitude. Here, we generated and integrated a multi-omics single-cell atlas of the adult human retina, including more than 250,000 nuclei for single-nuclei RNA-seq and 137,000 nuclei for single-nuclei ATAC-seq. Cross-species comparison of the retina atlas among human, monkey, mice, and chicken revealed relatively conserved and non-conserved types. Interestingly, the overall cell heterogeneity in primate retina decreases compared with that of rodent and chicken retina. Through integrative analysis, we identified 35,000 distal cis-element-gene pairs, constructed transcription factor (TF)-target regulons for more than 200 TFs, and partitioned the TFs into distinct co-active modules. We also revealed the heterogeneity of the cis-element-gene relationships in different cell types, even from the same class. Taken together, we present a comprehensive single-cell multi-omics atlas of the human retina as a resource that enables systematic molecular characterization at individual cell-type resolution.
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CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA sequencing.
Recent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method demonstrates comparable accuracy to and is markedly faster than the state-of-the-art software. Moreover, CellFishing.jl is scalable to more than one million cells, and the throughput of the search is approximately 1600 cells per second.
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A single-cell transcriptome atlas for zebrafish development.
The ability to define cell types and how they change during organogenesis is central to our understanding of animal development and human disease. Despite the crucial nature of this knowledge, we have yet to fully characterize all distinct cell types and the gene expression differences that generate cell types during development. To address this knowledge gap, we produced an atlas using single-cell RNA-sequencing methods to investigate gene expression from the pharyngula to early larval stages in developing zebrafish. Our single-cell transcriptome atlas encompasses transcriptional profiles from 44,102 cells across four days of development using duplicate experiments that confirmed high reproducibility. We annotated 220 identified clusters and highlighted several strategies for interrogating changes in gene expression associated with the development of zebrafish embryos at single-cell resolution. Furthermore, we highlight the power of this analysis to assign new cell-type or developmental stage-specific expression information to many genes, including those that are currently known only by sequence and/or that lack expression information altogether. The resulting atlas is a resource for biologists to generate hypotheses for functional analysis, which we hope integrates with existing efforts to define the diversity of cell-types during zebrafish organogenesis, and to examine the transcriptional profiles that produce each cell type over developmental time.
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Optimized protocol for mouse footpad immune cell isolation for single-cell RNA sequencing and flow cytometry.
Single-cell RNA sequencing (scRNA-seq) requires the preparation of a highly viable single-cell suspension to get reliable sequencing results. Here, we present a protocol for isolating mouse footpad leukocytes while maintaining high viability. We describe steps for footpad collection, enzymatic tissue dissociation, leukocyte isolation and purification, and cell fixation and preservation. We then detail combinatorial barcoding, library preparation, scRNA-seq, and data analysis. Cells can be used to generate a complete molecular atlas at the single cell level.
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Single-cell and spatial omics in plants: from cellular atlases to regulatory mechanisms.
Single-cell RNA sequencing (scRNA-seq) has transformed transcriptomic studies by enabling gene expression profiling at the resolution of individual cells within and across a broad range of tissue types, revealing cellular heterogeneity that is obscured in bulk tissue transcriptomes. Over the past decade, improvements in microfluidics and library preparation have drastically increased throughput, allowing tens of thousands of cells to be assayed in a single experiment. Although initially developed in animal systems, scRNA-seq has rapidly emerged as a powerful and widely adopted approach in plant biology. Beyond transcriptomics, the integration of single-cell data with chromatin accessibility, proteomics, metabolomics, and spatial omics is enabling a system-level understanding of plant gene regulation and cellular organization. Network-based analytical frameworks further support the reconstruction of gene regulatory networks and the interpretation of complex single-cell data. In this review, we summarize the current technological landscape of plant single-cell studies, discuss key experimental and analytical challenges, and review emerging strategies for validating single-cell discoveries. We also discuss future directions in applying single-cell technologies to woody perennials plants and bioenergy-relevant crops, emphasizing their potential to accelerate the discovery of cell type-specific regulatory mechanisms underlying growth, stress resilience, and biomass production.
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Intra- and extra-cranial BCOR-ITD tumours are separate entities within the BCOR-rearranged family.
BCOR-ITD tumours form an emerging family of aggressive entities with an internal tandem duplication (ITD) in the last exon of the BCOR gene. The family includes cerebral tumours, termed central nervous system BCOR-ITD (CNS BCOR-ITD), and sarcomatous types described in the kidney as clear cell sarcoma of the kidney (CCSK), in the endometrium as high-grade endometrial stromal sarcoma, and in the bone and soft tissue as undifferentiated round cell sarcoma or primitive myxoid mesenchymal tumour of infancy. Based on a series of 33 retrospective cases, including 10 CNS BCOR-ITD and 23 BCOR-ITD sarcomas, we interrogated the homogeneity of the entity regarding clinical, radiological, and histopathological findings, and molecular signatures. Whole-transcriptomic sequencing and DNA methylation profiling were used for unsupervised clustering. BCOR-ITD tumours mostly affected young children with a median age at diagnosis of 2.1 years (range 0-62.4). Median overall survival was 3.9 years and progression-free survival was 1.4 years. This dismal prognosis is shared among tumours in all locations except CCSK. Histopathological review revealed marked differences between CNS BCOR-ITD and BCOR-ITD sarcomas. These two groups were consistently segregated by unsupervised clustering of expression (n = 22) and DNA methylation (n = 21) data. Proximity between the two groups may result from common somatic changes within key pathways directly related to the novel activity of the ITD itself. Conversely, comparison of gene signatures with single-cell RNA-Seq atlases suggests that the distinction between BCOR-ITD sarcomas and CNS BCOR-ITD may result from differences in cells of origin.
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Single-cell RNA cap and tail sequencing (scRCAT-seq) reveals subtype-specific isoforms differing in transcript demarcation.
The differences in transcription start sites (TSS) and transcription end sites (TES) among gene isoforms can affect the stability, localization, and translation efficiency of mRNA. Gene isoforms allow a single gene diverse functions across different cell types, and isoform dynamics allow different functions over time. However, methods to efficiently identify and quantify RNA isoforms genome-wide in single cells are still lacking. Here, we introduce single cell RNA Cap And Tail sequencing (scRCAT-seq), a method to demarcate the boundaries of isoforms based on short-read sequencing, with higher efficiency and lower cost than existing long-read sequencing methods. In conjunction with machine learning algorithms, scRCAT-seq demarcates RNA transcripts with unprecedented accuracy. We identified hundreds of previously uncharacterized transcripts and thousands of alternative transcripts for known genes, revealed cell-type specific isoforms for various cell types across different species, and generated a cell atlas of isoform dynamics during the development of retinal cones.
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Quantification of escape from X chromosome inactivation with single-cell omics data reveals heterogeneity across cell types and tissues.
Several X-linked genes escape from X chromosome inactivation (XCI), while differences in escape across cell types and tissues are still poorly characterized. Here, we developed scLinaX for directly quantifying relative gene expression from the inactivated X chromosome with droplet-based single-cell RNA sequencing (scRNA-seq) data. The scLinaX and differentially expressed gene analyses with large-scale blood scRNA-seq datasets consistently identified the stronger escape in lymphocytes than in myeloid cells. An extension of scLinaX to a 10x multiome dataset (scLinaX-multi) suggested a stronger escape in lymphocytes than in myeloid cells at the chromatin-accessibility level. The scLinaX analysis of human multiple-organ scRNA-seq datasets also identified the relatively strong degree of escape from XCI in lymphoid tissues and lymphocytes. Finally, effect size comparisons of genome-wide association studies between sexes suggested the underlying impact of escape on the genotype-phenotype association. Overall, scLinaX and the quantified escape catalog identified the heterogeneity of escape across cell types and tissues.
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A unified analysis of atlas single-cell data.
Recent efforts to generate atlas-scale single-cell data provide opportunities for joint analysis across tissues and modalities. Existing methods use cells as the reference unit, hindering downstream gene-based analysis and removing genuine biological variation. Here we present GIANT, an integration method designed for atlas-scale gene analysis across cell types and tissues. GIANT converts data sets into gene graphs and recursively embeds genes without additional alignment. Applying GIANT to two recent atlas data sets yields unified gene-embedding spaces across human tissues and data modalities. Further evaluations demonstrate GIANT's usefulness in discovering diverse gene functions and underlying gene regulation in cells from different tissues.