Search

Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data., (© 2024. The Author(s).)

Individual cells are basic units of life. Despite extensive efforts to characterize the cellular heterogeneity of different organisms, cross-species comparisons of landscape dynamics have not been achieved. Here, we applied single-cell RNA sequencing (scRNA-seq) to map organism-level cell landscapes at multiple life stages for mice, zebrafish and Drosophila. By integrating the comprehensive dataset of > 2.6 million single cells, we constructed a cross-species cell landscape and identified signatures and common pathways that changed throughout the life span. We identified structural inflammation and mitochondrial dysfunction as the most common hallmarks of organism aging, and found that pharmacological activation of mitochondrial metabolism alleviated aging phenotypes in mice. The cross-species cell landscape with other published datasets were stored in an integrated online portal-Cell Landscape. Our work provides a valuable resource for studying lineage development, maturation and aging., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Diverse cell types in tissues have distinct gene expression programs, chromatin states, and nuclear architectures. To correlate such multimodal information across thousands of single cells in mouse brain tissue sections, we use integrated spatial genomics, imaging thousands of genomic loci along with RNAs and epigenetic markers simultaneously in individual cells. We reveal that cell type–specific association and scaffolding of DNA loci around nuclear bodies organize the nuclear architecture and correlate with differential expression levels in different cell types. At the submegabase level, active and inactive X chromosomes access similar domain structures in single cells despite distinct epigenetic and expression states. This work represents a major step forward in linking single-cell three-dimensional nuclear architecture, gene expression, and epigenetic modifications in a native tissue context.

Cell types are the basic building units of multicellular life, with extensive diversities. The evolution of cell types is a crucial layer of comparative cell biology but is thus far not comprehensively studied. We define a compendium of cell atlases using single-cell RNA-seq (scRNA-seq) data from seven animal species and construct a cross-species cell-type evolutionary hierarchy. We present a roadmap for the origin and diversity of major cell categories and find that muscle and neuron cells are conserved cell types. Furthermore, we identify a cross-species transcription factor (TF) repertoire that specifies major cell categories. Overall, our study reveals conservation and divergence of cell types during animal evolution, which will further expand the landscape of comparative genomics., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Identifying the relationships between chromosome structures, nuclear bodies, chromatin states and gene expression is an overarching goal of nuclear-organization studies 1-4 . Because individual cells appear to be highly variable at all these levels 5 , it is essential to map different modalities in the same cells. Here we report the imaging of 3,660 chromosomal loci in single mouse embryonic stem (ES) cells using DNA seqFISH+, along with 17 chromatin marks and subnuclear structures by sequential immunofluorescence and the expression profile of 70 RNAs. Many loci were invariably associated with immunofluorescence marks in single mouse ES cells. These loci form 'fixed points' in the nuclear organizations of single cells and often appear on the surfaces of nuclear bodies and zones defined by combinatorial chromatin marks. Furthermore, highly expressed genes appear to be pre-positioned to active nuclear zones, independent of bursting dynamics in single cells. Our analysis also uncovered several distinct mouse ES cell subpopulations with characteristic combinatorial chromatin states. Using clonal analysis, we show that the global levels of some chromatin marks, such as H3 trimethylation at lysine 27 (H3K27me3) and macroH2A1 (mH2A1), are heritable over at least 3-4 generations, whereas other marks fluctuate on a faster time scale. This seqFISH+-based spatial multimodal approach can be used to explore nuclear organization and cell states in diverse biological systems.

Background: Single-cell RNA-sequencing technologies provide a powerful tool for systematic dissection of cellular heterogeneity. However, the prevalence of dropout events imposes complications during data analysis and, despite numerous efforts from the community, this challenge has yet to be solved., Results: Here we present a computational method, called RESCUE, to mitigate the dropout problem by imputing gene expression levels using information from other cells with similar patterns. Unlike existing methods, we use an ensemble-based approach to minimize the feature selection bias on imputation. By comparative analysis of simulated and real single-cell RNA-seq datasets, we show that RESCUE outperforms existing methods in terms of imputation accuracy which leads to more precise cell-type identification., Conclusions: Taken together, these results suggest that RESCUE is a useful tool for mitigating dropouts in single-cell RNA-seq data. RESCUE is implemented in R and available at https://github.com/seasamgo/rescue .

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly, our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations.

Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data. We have tested STREAM on several synthetic and real datasets generated with different single-cell technologies. We further demonstrate its utility for understanding myoblast differentiation and disentangling known heterogeneity in hematopoiesis for different organisms. STREAM is an open-source software package.

Imaging the transcriptome in situ with high accuracy has been a major challenge in single-cell biology, which is particularly hindered by the limits of optical resolution and the density of transcripts in single cells 1-5 . Here we demonstrate an evolution of sequential fluorescence in situ hybridization (seqFISH+). We show that seqFISH+ can image mRNAs for 10,000 genes in single cells-with high accuracy and sub-diffraction-limit resolution-in the cortex, subventricular zone and olfactory bulb of mouse brain, using a standard confocal microscope. The transcriptome-level profiling of seqFISH+ allows unbiased identification of cell classes and their spatial organization in tissues. In addition, seqFISH+ reveals subcellular mRNA localization patterns in cells and ligand-receptor pairs across neighbouring cells. This technology demonstrates the ability to generate spatial cell atlases and to perform discovery-driven studies of biological processes in situ.

The exact identity of castrate-resistant (CR) cells and their relation to CR prostate cancer (CRPC) is unresolved. We use single-cell gene profiling to analyze the molecular heterogeneity in basal and luminal compartments. Within the luminal compartment, we identify a subset of cells intrinsically resistant to castration with a bi-lineage gene expression pattern. We discover LY6D as a marker of CR prostate progenitors with multipotent differentiation and enriched organoid-forming capacity. Lineage tracing further reveals that LY6D + CR luminal cells can produce LY6D - luminal cells. In contrast, in luminal cells lacking PTEN, LY6D + cells predominantly give rise to LY6D + tumor cells, contributing to high-grade PIN lesions. Gene expression analyses in patients' biopsies indicate that LY6D expression correlates with early disease progression, including progression to CRPC. Our studies thus identify a subpopulation of luminal progenitors characterized by LY6D expression and intrinsic castration resistance. LY6D may serve as a prognostic maker for advanced prostate cancer., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Single-cell analysis is a powerful tool for dissecting the cellular composition within a tissue or organ. However, it remains difficult to detect rare and common cell types at the same time. Here, we present a new computational method, GiniClust2, to overcome this challenge. GiniClust2 combines the strengths of two complementary approaches, using the Gini index and Fano factor, respectively, through a cluster-aware, weighted ensemble clustering technique. GiniClust2 successfully identifies both common and rare cell types in diverse datasets, outperforming existing methods. GiniClust2 is scalable to large datasets.

Single-cell RNA sequencing (scRNA-seq) technologies are poised to reshape the current cell-type classification system. However, a transcriptome-based single-cell atlas has not been achieved for complex mammalian systems. Here, we developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices. Using Microwell-seq, we analyzed more than 400,000 single cells covering all of the major mouse organs and constructed a basic scheme for a mouse cell atlas (MCA). We reveal a single-cell hierarchy for many tissues that have not been well characterized previously. We built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression. Our study demonstrates the wide applicability of the Microwell-seq technology and MCA resource., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The advent of single-cell sequencing has been revolutionary to the field of cancer genomics. Perfectly suited to capture cancer's heterogeneous nature, single-cell analyses provide information bulk sequencing could never hope to uncover. Many mechanisms of cancer have yet to be fully understood, and single-cell approaches are showing promise in their abilities to uncover these mysteries. Here we focus on the most recent single-cell methods for cancer genomics, and how they are not only providing insights into the inner workings of cancer, but are also transforming individualized therapy and non-invasive monitoring and diagnosis., (Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into account in current methods. We present ECLAIR (Ensemble Cell Lineage Analysis with Improved Robustness), a novel computational method for the statistical inference of cell lineage relationships from single-cell gene expression data. ECLAIR uses an ensemble approach to improve the robustness of lineage predictions, and provides a quantitative estimate of the uncertainty of lineage branchings. We show that the application of ECLAIR to published datasets successfully reconstructs known lineage relationships and significantly improves the robustness of predictions. ECLAIR is a powerful bioinformatics tool for single-cell data analysis. It can be used for robust lineage reconstruction with quantitative estimate of prediction accuracy., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

High-throughput single-cell technologies have great potential to discover new cell types; however, it remains challenging to detect rare cell types that are distinct from a large population. We present a novel computational method, called GiniClust, to overcome this challenge. Validation against a benchmark dataset indicates that GiniClust achieves high sensitivity and specificity. Application of GiniClust to public single-cell RNA-seq datasets uncovers previously unrecognized rare cell types, including Zscan4-expressing cells within mouse embryonic stem cells and hemoglobin-expressing cells in the mouse cortex and hippocampus. GiniClust also correctly detects a small number of normal cells that are mixed in a cancer cell population.

Variation in gene expression is an important feature of mouse embryonic stem cells (ESCs). However, the mechanisms responsible for global gene expression variation in ESCs are not fully understood. We performed single-cell mRNA-seq analysis of mouse ESCs and uncovered significant heterogeneity in ESCs cultured in serum. We define highly variable gene clusters with distinct chromatin states and show that bivalent genes are prone to expression variation. At the same time, we identify an ESC-priming pathway that initiates the exit from the naive ESC state. Finally, we provide evidence that a large proportion of intracellular network variability is due to the extracellular culture environment. Serum-free culture reduces cellular heterogeneity and transcriptome variation in ESCs., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Genetic changes and environmental differences result in cellular heterogeneity among cancer cells within the same tumor, thereby complicating treatment outcomes. Recent advances in single-cell technologies have opened new avenues to characterize the intra-tumor cellular heterogeneity, identify rare cell types, measure mutation rates, and, ultimately, guide diagnosis and treatment. In this paper we review the recent single-cell technological and computational advances at the genomic, transcriptomic, and proteomic levels, and discuss their applications in cancer research., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Background: A fundamental challenge for cancer therapy is that each tumor contains a highly heterogeneous cell population whose structure and mechanistic underpinnings remain incompletely understood. Recent advances in single-cell gene expression profiling have created new possibilities to characterize this heterogeneity and to dissect the potential intra-cancer cellular hierarchy., Results: Here, we apply single-cell analysis to systematically characterize the heterogeneity within leukemic cells using the MLL-AF9 driven mouse model of acute myeloid leukemia. We start with fluorescence-activated cell sorting analysis with seven surface markers, and extend by using a multiplexing quantitative polymerase chain reaction approach to assay the transcriptional profile of a panel of 175 carefully selected genes in leukemic cells at the single-cell level. By employing a set of computational tools we find striking heterogeneity within leukemic cells. Mapping to the normal hematopoietic cellular hierarchy identifies two distinct subtypes of leukemic cells; one similar to granulocyte/monocyte progenitors and the other to macrophage and dendritic cells. Further functional experiments suggest that these subtypes differ in proliferation rates and clonal phenotypes. Finally, co-expression network analysis reveals similarities as well as organizational differences between leukemia and normal granulocyte/monocyte progenitor networks., Conclusions: Overall, our single-cell analysis pinpoints previously uncharacterized heterogeneity within leukemic cells and provides new insights into the molecular signatures of acute myeloid leukemia.

Stem cell differentiation pathways are most often studied at the population level, whereas critical decisions are executed at the level of single cells. We have established a highly multiplexed, quantitative PCR assay to profile in an unbiased manner a panel of all commonly used cell surface markers (280 genes) from individual cells. With this method, we analyzed over 1,500 single cells throughout the mouse hematopoietic system and illustrate its utility for revealing important biological insights. The comprehensive single cell data set permits mapping of the mouse hematopoietic stem cell differentiation hierarchy by computational lineage progression analysis. Further profiling of 180 intracellular regulators enabled construction of a genetic network to assign the earliest differentiation event during hematopoietic lineage specification. Analysis of acute myeloid leukemia elicited by MLL-AF9 uncovered a distinct cellular hierarchy containing two independent self-renewing lineages with different clonal activities. The strategy has broad applicability in other cellular systems., (Copyright © 2013 Elsevier Inc. All rights reserved.)