A fast, scalable and versatile tool for analysis of single-cell omics data

A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection

Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type-specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.

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.

Small data methods in omics: the power of one

Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.

Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome

Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Pseudotime analysis of perturbations connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state.

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.

OpenTn5: Open-Source Resource for Robust and Scalable Tn5 Transposase Purification and Characterization

Tagmentation combines DNA fragmentation and sequencing adapter addition by leveraging the transposition activity of the bacterial cut-and-paste Tn5 transposase, to enable efficient sequencing library preparation. Here we present an open-source protocol for the generation of multi-purpose hyperactive Tn5 transposase, including its benchmarking in CUT&Tag, bulk and single-cell ATAC-seq. The OpenTn5 protocol yields multi-milligram quantities of pG-Tn5E54K, L372P protein per liter of E. coli culture, sufficient for thousands of tagmentation reactions and the enzyme retains activity in storage for more than a year.

Bigtools: a high-performance BigWig and BigBed library in Rust

MOTIVATION

The BigWig and BigBed file formats were originally designed for the visualization of next-generation sequencing data through a genome browser. Due to their versatility, these formats have long since become ubiquitous for the storage of processed sequencing data and regularly serve as the basis for downstream data analysis. As the number and size of sequencing experiments continues to accelerate, there is an increasing demand to efficiently generate and query BigWig and BigBed files in a scalable and robust manner, and to efficiently integrate these functionalities into data analysis environments and third-party applications.

RESULTS

Here, we present Bigtools, a feature-complete, high-performance, and integrable software library for generating and querying both BigWig and BigBed files. Bigtools is written in the Rust programming language and includes a flexible suite of command line tools as well as bindings to Python.

AVAILABILITY AND IMPLEMENTATION

Bigtools is cross-platform and released under the MIT license. It is distributed on Crates.io, Bioconda, and the Python Package Index, and the source code is available at https://github.com/jackh726/bigtools.

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases

Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data

SUMMARY

Recent technical advancements in single-cell chromatin accessibility sequencing (scCAS) have brought new insights to the characterization of epigenetic heterogeneity. As single-cell genomics experiments scale up to hundreds of thousands of cells, the demand for computational resources for downstream analysis grows intractably large and exceeds the capabilities of most researchers. Here, we propose EpiCarousel, a tailored Python package based on lazy loading, parallel processing, and community detection for memory- and time-efficient identification of metacells, i.e. the emergence of homogenous cells, in large-scale scCAS data. Through comprehensive experiments on five datasets of various protocols, sample sizes, dimensions, number of cell types, and degrees of cell-type imbalance, EpiCarousel outperformed baseline methods in systematic evaluation of memory usage, computational time, and multiple downstream analyses including cell type identification. Moreover, EpiCarousel executes preprocessing and downstream cell clustering on the atlas-level dataset with 707 043 cells and 1 154 611 peaks within 2 h consuming <75 GB of RAM and provides superior performance for characterizing cell heterogeneity than state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

The EpiCarousel software is well-documented and freely available at https://github.com/biox-nku/epicarousel. It can be seamlessly interoperated with extensive scCAS analysis toolkits.

Bigtools: a high-performance BigWig and BigBed library in Rust

The BigWig and BigBed file formats were originally designed for the visualization of next-generation sequencing data through a genome browser. Due to their versatility, these formats have long since become ubiquitous for the storage of processed sequencing data and regularly serve as the basis for downstream data analysis. As the number and size of sequencing experiments continues to accelerate, there is an increasing demand to efficiently generate and query BigWig and BigBed files in a scalable and robust manner, and to efficiently integrate these functionalities into data analysis environments and third-party applications. Here, we present Bigtools, a feature-complete, high-performance, and integrable software library for generating and querying both BigWig and BigBed files. Bigtools is written in the Rust programming language and includes a flexible suite of command line tools as well as bindings to Python. Bigtools is cross-platform and released under the MIT license. It is distributed on Crates.io and the Python Package Index, and the source code is available at https://github.com/jackh726/bigtools.

Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects

The Single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) has gained increasing popularity in recent years, allowing for chromatin accessibility to be deciphered and gene regulatory networks (GRNs) to be inferred at single-cell resolution. This cutting-edge technology now enables the genome-wide profiling of chromatin accessibility at the cellular level and the capturing of cell-type-specific cis-regulatory elements (CREs) that are masked by cellular heterogeneity in bulk assays. Additionally, it can also facilitate the identification of rare and new cell types based on differences in chromatin accessibility and the charting of cellular developmental trajectories within lineage-related cell clusters. Due to technical challenges and limitations, the data generated from scATAC-seq exhibit unique features, often characterized by high sparsity and noise, even within the same cell type. To address these challenges, various bioinformatic tools have been developed. Furthermore, the application of scATAC-seq in plant science is still in its infancy, with most research focusing on root tissues and model plant species. In this review, we provide an overview of recent progress in scATAC-seq and its application across various fields. We first conduct scATAC-seq in plant science. Next, we highlight the current challenges of scATAC-seq in plant science and major strategies for cell type annotation. Finally, we outline several future directions to exploit scATAC-seq technologies to address critical challenges in plant science, ranging from plant ENCODE(The Encyclopedia of DNA Elements) project construction to GRN inference, to deepen our understanding of the roles of CREs in plant biology.