Resolution Of Individual Cells Research Articles

Scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution.

Understanding how regulatory DNA elements shape gene expression across individual cells is a fundamental challenge in genomics. Joint RNA-seq and epigenomic profiling provides opportunities to build unifying models of gene regulation capturing sequence determinants across steps of gene expression. However, current models, developed primarily for bulk omics data, fail to capture the cellular heterogeneity and dynamic processes revealed by single-cell multi-modal technologies. Here, we introduce scooby, the first model to predict scRNA-seq coverage and scATAC-seq insertion profiles along the genome from sequence at single-cell resolution. For this, we leverage the pre-trained multi-omics profile predictor Borzoi as a foundation model, equip it with a cell-specific decoder, and fine-tune its sequence embeddings. Specifically, we condition the decoder on the cell position in a precomputed single-cell embedding resulting in strong generalization capability. Applied to a hematopoiesis dataset, scooby recapitulates cell-specific expression levels of held-out genes and cells, and identifies regulators and their putative target genes through in silico motif deletion. Moreover, accurate variant effect prediction with scooby allows for breaking down bulk eQTL effects into single-cell effects and delineating their impact on chromatin accessibility and gene expression. We anticipate scooby to aid unraveling the complexities of gene regulation at the resolution of individual cells.

Single-cell gene-expression measurements in Vibrio cholerae biofilms reveal spatiotemporal patterns underlying development.

Bacteria commonly exist in multicellular, surface-attached communities called biofilms. Biofilms are central to ecology, medicine, and industry. The Vibrio cholerae pathogen forms biofilms from single founder cells that, via cell division, mature into three-dimensional structures with distinct, yet reproducible, regional architectures. To define mechanisms underlying biofilm developmental transitions, we establish a single-molecule fluorescence in situ hybridization (smFISH) approach that enables accurate quantitation of spatiotemporal gene-expression patterns in biofilms at individual-cell resolution. smFISH analyses of V. cholerae biofilm regulatory and structural genes demonstrate that, as biofilms mature, matrix gene expression decreases, and simultaneously, a pattern emerges in which matrix gene expression is largely confined to peripheral biofilm cells. Both quorum sensing and c-di-GMP-signaling are required to generate the proper temporal pattern of matrix gene expression, while c-di-GMP-signaling sets the regional expression pattern without input from quorum sensing. The smFISH strategy provides insight into mechanisms conferring particular fates to individual biofilm cells.

Abstract 2928: Solving the cancer mutation conundrum: A single cell, massively parallel approach for cancer mutation discovery, genome modelling and functional characterization

Abstract Large scale genomic studies are cataloguing tens of thousands of cancer mutations across a wide spectrum of tumor types. Conventional DNA sequencing methods have limitations in terms of identify mutations at high resolution among single cells. Moreover, determining cancer mutations’ phenotype and function remains an enormous challenge - most mutations are assigned to the category of variants of unknown significance. In-silico functional predictions of cancer mutations are frequently used as a stopgap solution. However, these computational methods lack the ability to make refined biological and functional predictions. We demonstrated a new single cell experimental strategy that accelerates the discovery of cancer mutations and their characterization. This genomic technology framework does the following: (1) identify disease-related mutations at the resolution of individual cells from tumors or cell lines; (2) genome engineer these candidate mutations into cellular experimental systems for further biological characterization. First, we developed a single cell approach to identify cancer somatic mutations and even gene fusion rearrangements found in coding regions of mRNAs. Primary tumors are processed into single cell RNA-seq libraries and analyzed with nanopore long read sequencing. Across thousands of cells from primary tumors, we identified single cell mutations from the nanopore long reads that covered cancer gene transcripts. This approach allows us to even identify cancer rearrangements at single cell resolution. Next, we used short-read single cell transcriptomes to characterize the cell type and determine the gene expression characteristics of those cells with mutations. For single cell phenotypic studies, we developed a highly multiplexed technology using CRISPR base editors to directly engineer hundreds of mutations into their original genes among single cells and determine their transcriptional phenotype. Among specific individual cells, nanopore-based long-read sequencing identifies these modelled mutations directly from a target’s transcript sequence. Then, we integrate this single cell mutation data with the short-read transcriptome profile from the same individual cell. Therefore, we determine both the genotype and phenotype of a given engineered mutation from an individual cell. In summary, our work demonstrates a new, direct and highly scalable method for identifying, modelling and functionally assessing cancer mutation phenotype at single cell resolution. This approach enables the broader assessment of cancer mutations impact on tumor biology and clinical ramifications. Citation Format: Susan M. Grimes, Heonseok Kim, Sharmili Roy, Anuja Sathe, Billy T. Lau, Hanlee P. Ji. Solving the cancer mutation conundrum: A single cell, massively parallel approach for cancer mutation discovery, genome modelling and functional characterization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2928.

Massive and efficient encapsulation of single cells in monodisperse droplets and collagen-alginate microgels using a microfluidic device.

Single-cell manipulation is the key foundation of life exploration at individual cell resolution. Constructing easy-to-use, high-throughput, and biomimetic manipulative tools for efficient single-cell operation is quite necessary. In this study, a facile and efficient encapsulation of single cells relying on the massive and controllable production of droplets and collagen-alginate microgels using a microfluidic device is presented. High monodispersity and geometric homogeneity of both droplet and microgel generation were experimentally demonstrated based on the well-investigated microfluidic fabricating procedure. The reliability of the microfluidic platform for controllable, high-throughput, and improved single-cell encapsulation in monodisperse droplets and microgels was also confirmed. A single-cell encapsulation rate of up to 33.6% was achieved based on the established microfluidic operation. The introduction of stromal material in droplets/microgels for encapsulation provided single cells an in vivo simulated microenvironment. The single-cell operation achievement offers a methodological approach for developing simple and miniaturized devices to perform single-cell manipulation and analysis in a high-throughput and microenvironment-biomimetic manner. We believe that it holds great potential for applications in precision medicine, cell microengineering, drug discovery, and biosensing.

Cell population growth kinetics in the presence of stochastic heterogeneity of cell phenotype

Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized uniform exponential growth of the cell population. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture a departure from the uniform exponential growth model for the initial growth (“take-off”). Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth dynamics, which could be explained by the presence of inter-converting subpopulations with different growth rates, and which could last for many generations. Based on the hypothesis of existence of multiple subpopulations, we developed a branching process model that was consistent with the experimental observations.

Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects

Differential allele-specific expression (ASE) is a powerful tool to study context-specific cis-regulation of gene expression. Such effects can reflect the interaction between genetic or epigenetic factors and a measured context or condition. Single-cell RNA sequencing (scRNA-seq) allows the measurement of ASE at individual-cell resolution, but there is a lack of statistical methods to analyze such data. We present Differential Allelic Expression using Single-Cell data (DAESC), a powerful method for differential ASE analysis using scRNA-seq from multiple individuals, with statistical behavior confirmed through simulation. DAESC accounts for non-independence between cells from the same individual and incorporates implicit haplotype phasing. Application to data from 105 induced pluripotent stem cell (iPSC) lines identifies 657 genes dynamically regulated during endoderm differentiation, with enrichment for changes in chromatin state. Application to a type-2 diabetes dataset identifies several differentially regulated genes between patients and controls in pancreatic endocrine cells. DAESC is a powerful method for single-cell ASE analysis and can uncover novel insights on gene regulation.

ScBKAP: A Clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means.

Advances in single-cell RNA sequencing (scRNA-seq) technologies allow researchers to analyze the genome-wide transcription profile and to solve biological problems at the individual-cell resolution. However, existing clustering methods on scRNA-seq suffer from high dropout rate and curse of dimensionality in the data. Here, we propose a novel pipeline, scBKAP, the cornerstone of which is a single-cell bisecting K-means clustering method based on an autoencoder network and a dimensionality reduction model MPDR. Specially, scBKAP utilizes an autoencoder network to reconstruct gene expression values from scRNA-seq data to alleviate the dropout issue, and the MPDR model composed of the M3Drop feature selection algorithm and the PHATE dimensionality reduction algorithm to reduce the dimensions of reconstructed data. The dimensionality-reduced data are then fed into the bisecting K-means clustering algorithm to identify the clusters of cells. Comprehensive experiments demonstrate scBKAP's superior performance over nine state-of-the-art single-cell clustering methods on 21 public scRNA-seq datasets and simulated datasets. The source codes and datasets are available at https://github.com/YuBinLab-QUST/scBKAP/ and https://doi.org/10.24433/CO.4592131.v1.

A robust and accurate single-cell data trajectory inference method using ensemble pseudotime

BackgroundThe advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in individual cell resolution. In recent years, many trajectory inference methods have been developed. They have focused on using the graph method to infer the trajectory using single-cell data, and then calculate the geodesic distance as the pseudotime. However, these methods are vulnerable to errors caused by the inferred trajectory. Therefore, the calculated pseudotime suffers from such errors.Results We proposed a novel framework for trajectory inference called the single-cell data Trajectory inference method using Ensemble Pseudotime inference (scTEP). scTEP utilizes multiple clustering results to infer robust pseudotime and then uses the pseudotime to fine-tune the learned trajectory. We evaluated the scTEP using 41 real scRNA-seq data sets, all of which had the ground truth development trajectory. We compared the scTEP with state-of-the-art methods using the aforementioned data sets. Experiments on real linear and non-linear data sets demonstrate that our scTEP performed superior on more data sets than any other method. The scTEP also achieved a higher average and lower variance on most metrics than other state-of-the-art methods. In terms of trajectory inference capacity, the scTEP outperforms those methods. In addition, the scTEP is more robust to the unavoidable errors resulting from clustering and dimension reduction.ConclusionThe scTEP demonstrates that utilizing multiple clustering results for the pseudotime inference procedure enhances its robustness. Furthermore, robust pseudotime strengthens the accuracy of trajectory inference, which is the most crucial component in the pipeline. scTEP is available at https://cran.r-project.org/package=scTEP.

EPCO-27. REVEALING TUMOR HETEROGENEITY AND IMMUNE MICROENVIRONMENT IN NF-1 GLIOMA BY SINGLE-CELL GENE EXPRESSION PROFILING

Abstract Germline mutations in the NF1 tumor suppressor gene cause neurofibromatosis type 1 (NF-1), the most common human genetic cancer predisposition syndrome. NF1 patients are prone to develop peripheral and central nervous system tumors, with glioma occurring in 15-20% of patients. The molecular profiles of glioma from patients with NF-1 have been previously explored by large scale DNA and RNA bulk analyses, providing the first comprehensive genetic landscape of NF-1 glioma. However, the intra-tumoral architecture and the microenvironment organization remained largely concealed within the global bulk profile. Here, to characterize the complexity of molecular states of both malignant and non-malignant cells, we profiled NF-1 glioma gene expression at individual cell resolution. We performed single-nuclei RNA sequencing (Nucseq) on frozen NF-1 glioma specimens, including high-grade and low-grade tumors. The functional classification of the transcriptional programs activated in the individual tumor cells identified three main molecular states that partially overlapped with the cellular subtypes of sporadic glioblastoma. Tumor cells of low-grade NF-1 glioma exhibited marked enrichment of neuronal functions, suggesting that the gliomagenesis process in the context of NF1 inactivation might be supported by specialized neuronal activities. Conversely, the proliferative state characterized high-grade NF-1 glioma, indicating that cell cycle-associated pathways are drivers of tumor progression. The single-cell deconvolution of immune microenvironment in NF-1 low grade glioma revealed infiltration of CD8+ T lymphocytes and the active crosstalk of these cells with pro-inflammatory macrophages and mature dendritic cells, supporting a possible anti-tumor cell-mediated response. The identification of divergent tumor cell states as well as the characterization of the immune infiltrates provide new insights for accurate stratification of NF-1 glioma patients and to inform personalized therapeutics.

ScWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information.

Single-cell RNA sequencing (scRNA-seq) can provide insight into gene expression patterns at the resolution of individual cells, which offers new opportunities to study the behavior of different cell types. However, it is often plagued by dropout events, a phenomenon where the expression value of a gene tends to be measured as zero in the expression matrix due to various technical defects. In this article, we argue that borrowing gene and cell information across column and row subspaces directly results in suboptimal solutions due to the noise contamination in imputing dropout values. Thus, to impute more precisely the dropout events in scRNA-seq data, we develop a regularization for leveraging that imperfect prior information to estimate the true underlying prior subspace and then embed it in a typical low-rank matrix completion-based framework, named scWMC. To evaluate the performance of the proposed method, we conduct comprehensive experiments on simulated and real scRNA-seq data. Extensive data analysis, including simulated analysis, cell clustering, differential expression analysis, functional genomic analysis, cell trajectory inference and scalability analysis, demonstrate that our method produces improved imputation results compared to competing methods that benefits subsequent downstream analysis. The source code is available at https://github.com/XuYuanchi/scWMC and test data is available at https://doi.org/10.5281/zenodo.6832477. Supplementary data are available at Bioinformatics online.

ALTEN: A High-Fidelity Primary Tissue-Engineering Platform to Assess Cellular Responses Ex Vivo.

To fully investigate cellular responses to stimuli and perturbations within tissues, it is essential to replicate the complex molecular interactions within the local microenvironment of cellular niches. Here, the authors introduce Alginate‐based tissue engineering (ALTEN), a biomimetic tissue platform that allows ex vivo analysis of explanted tissue biopsies. This method preserves the original characteristics of the source tissue's cellular milieu, allowing multiple and diverse cell types to be maintained over an extended period of time. As a result, ALTEN enables rapid and faithful characterization of perturbations across specific cell types within a tissue. Importantly, using single‐cell genomics, this approach provides integrated cellular responses at the resolution of individual cells. ALTEN is a powerful tool for the analysis of cellular responses upon exposure to cytotoxic agents and immunomodulators. Additionally, ALTEN's scalability using automated microfluidic devices for tissue encapsulation and subsequent transport, to enable centralized high‐throughput analysis of samples gathered by large‐scale multicenter studies, is shown.

Single-cell RNA sequencing data analysis based on non-uniform ε-neighborhood network.

Single-cell RNA sequencing (scRNA-seq) technology provides the possibility to study cell heterogeneity and cell development on the resolution of individual cells. Arguably, three of the most important computational targets on scRNA-seq data analysis are data visualization, cell clustering and trajectory inference. Although a substantial number of algorithms have been developed, most of them do not treat the three targets in a systematic or consistent manner. In this article, we propose an efficient scRNA-seq analysis framework, which accomplishes the three targets consistently by non-uniform ε-neighborhood (NEN) network. First, a network is generated by our NEN method, which combines the advantages of both k-nearest neighbors (KNN) and ε-neighborhood (EN) to represent the manifold that data points reside in gene space. Then from such a network, we use its layout, its community and further its shortest path to achieve the purpose of scRNA-seq data visualization, clustering and trajectory inference. The results on both synthetic and real datasets indicate that our NEN method not only can visually provide the global topological structure of a dataset accurately compared with t-SNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection), but also has superior performances on clustering and pseudotime ordering of cells over the existing approaches. This analysis method has been made into a python package called ccnet and is freely available at https://github.com/Just-Jia/ccNet. Supplementary data are available at Bioinformatics online.

Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data

BackgroundExisting computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation.ResultsIn this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell–cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell–cell crosstalk.ConclusionsTo the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

The role of single-cell sequencing in studying tumour evolution.

Tumour evolution is a complex interplay between the acquisition of somatic (epi)genomic changes in tumour cells and the phenotypic consequences they cause, all in the context of a changing microenvironment. Single-cell sequencing offers a window into this dynamic process at the ultimate resolution of individual cells. In this review, we discuss the transformative insight offered by single-cell sequencing technologies for understanding tumour evolution.

Unlocking immune-mediated disease mechanisms with transcriptomics.

The transcriptome represents the entire set of RNA transcripts expressed in a cell, reflecting both the underlying genetic and epigenetic landscape and environmental influences, providing a comprehensive view of functional cellular states at any given time. Recent technological advances now enable the study of the transcriptome at the resolution of individual cells, providing exciting opportunities to characterise cellular and molecular events that underpin immune-medicated diseases. Here, we draw on recent examples from the literature to highlight the application of advanced bioinformatics tools to extract mechanistic insight and disease biology from bulk and single-cell transcriptomic profiles. Key considerations for the use of available analysis techniques are presented throughout.

ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS

Time-series single-cell RNA sequencing (scRNA-seq) provides a breakthrough in modern biology by enabling researchers to profile and study the dynamics of genes and cells based on samples obtained from multiple time points at an individual cell resolution. However, cell asynchrony and an additional dimension of multiple time points raises challenges in the effective use of time-series scRNA-seq data for identifying genes and cell subclusters that vary over time. However, no effective tools are available. Here, we propose scTITANS (https://github.com/ZJUFanLab/scTITANS), a method that takes full advantage of individual cells from all time points at the same time by correcting cell asynchrony using pseudotime from trajectory inference analysis. By introducing a time-dependent covariate based on time-series analysis method, scTITANS performed well in identifying differentially expressed genes and cell subclusters from time-series scRNA-seq data based on several example datasets. Compared to current attempts, scTITANS is more accurate, quantitative, and capable of dealing with heterogeneity among cells and making full use of the timing information hidden in biological processes. When extended to broader research areas, scTITANS will bring new breakthroughs in studies with time-series single cell RNA sequencing data.

GCT-62. DISSECTING INTRATUMORAL HETEROGENEITY OF CENTRAL NERVOUS SYSTEM GERM CELL TUMORS BY SINGLE-CELL RNA-SEQUENCING

BACKGROUNDCentral nervous system germ cell tumor (CNSGCT) is a rare pediatric brain tumor. However, they are found at a relatively high incidence in East Asia. Germinoma is sensitive toward radiotherapy and chemotherapy; however, non-germinoma GCTs (NGGCT) often show poor response. Some cases are a mixture of germinoma and NGGCT (mixed GCT), and they sometimes change histological subtypes at recurrence. Previous report demonstrated that a germinoma and NGGCT component within the same mixed GCT tissue shared the same gene mutation, whereas the genome-wide methylation profiles were distinct from each other. The methylation profiles of germinoma was similar to the primordial germ cells (PGC) at the migration phase, supporting a model that PGC is the cell of origin for CNSGCT. However, tumor heterogeneity hinder information of the mixed bulk RNA-sequence data, causing difficulty in elucidating the mechanism of tumor development. The purpose of this study was to investigate the tumor cells subpopulations at the resolution of individual cells by single-cell RNA-seq.RESULTSFresh surgical tumor tissue was immediately dissociated mechanically and enzymatically. Tumor cells are separated from CD45-labelled lymphocytes by FACS, and libraries were generated by Chromium Single cell 3’ Reagent Kit. Total of 11 tumor samples were collected and sequenced. Unsupervised Clustering showed individual clusters. One of the clusters had high expression of Oct-4, which is a marker of germinoma. The other clusters showed different subtypes of cells representing the heterogeneity of CNSGCT. Further analysis including a pseudo-time course analysis is underway to identify the lineage of tumor cell development.

ARHGAP36 regulates proliferation and migration in papillary thyroid carcinoma cells.

The diagnosis and treatment of recurrence and metastasis in papillary thyroid carcinoma (PTC) are still clinical challenges. One of the key factors is the lack of specific diagnostic markers and therapeutic targets for recurrence and metastasis. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful approach to find specific biomarkers by dissecting expression profiling in human cancers at the resolution of individual cells. Here, we investigated cell profiles of the primary tumor and lymph node metastasis and paracancerous normal tissues in one PTC patient using scRNA-seq, and compared individual cell gene expression differences. The transcriptomes of 11,805 single cells were profiled, and malignant cells exhibited a profound transcriptional overlap between primary and metastatic lesions, but there were differences in the composition and quantity of non-malignant cells. ARHGAP36 was one of the genes that were highly expressed in almost all of the primary and metastatic malignant cells without non-malignant or normal follicular cells and was then confirmed by immunostaining in a sample cohort. Compared with the paracancerous normal tissue, the expression of ARHGAP36 in primary and metastatic carcinoma tissues was significantly higher as assayed by qRT-PCR. ARHGAP36 knockdown significantly inhibited the proliferation and migration of PTC cells in vitro and involved several proliferation and migration-associated signaling pathways by RNA seq. Our study demonstrated that ARHGAP36 is exclusively expressed in the malignant cells of primary PTC, as well as metastatic lesions, and regulates their proliferation and migration, meaning it can be used as a potential diagnostic marker and therapeutic target molecule.

Single-Cell Analysis of Foot-and-Mouth Disease Virus.

With the rapid development of single-cell technologies, the mechanisms underlying viral infections and the interactions between hosts and viruses are starting to be explored at the single-cell level. The foot-and-mouth-disease (FMD) virus (FMDV) causes an acute and persistent infection that can result in the break-out of FMD, which can have serious effects on animal husbandry. Single-cell techniques have emerged as powerful approaches to analyze virus infection at the resolution of individual cells. In this review, the existing single-cell studies examining FMDV will be systematically summarized, and the central themes of these studies will be presented.