CASB: a concanavalin A-based sample barcoding strategy for single-cell sequencing

High-throughput and multimodal profiling of antigen-specific T cells with a droplet-based cell-cell interaction screening platform

Identifying antigen-specific T cells from tumor-infiltrating lymphocytes is essential for designing effective T cell immunotherapies. Traditional methods can detect antigen-specific T cells but struggle with high-throughput screening and multimodal profiling simultaneously. To address this issue, we developed DropCCI, a new strategy that transfers antigen information to co-incubated T cells for high-throughput, non-contaminated multimodal profiling. In DropCCI, droplets encapsulated DNA barcodes and antigen-loaded antigen-presenting cells (APCs), while click chemistry-modified T cells were injected into these droplets to capture free barcodes and acquire the corresponding antigen information. Following cell-cell interaction, APCs were removed via streptavidin-biotin conjugation, to prevent contamination. The resulting T cells underwent single-cell omics sequencing for comprehensive profiling of their antigen specificity, transcriptome, and genomics accurately. This click-chemistry method allowed detection of antigen-specific T cells without lysing APCs, avoiding cross-cell contamination and enabling low-noise multimodal profiling of primary T cells. With a completion time within 12 h and no requirement for complex equipment, DropCCI provides unbiased single-cell sequencing results that offer a comprehensive understanding of anti-tumor T cell responses. The concept of DropCCI holds great promise not only for advancing the field of T cell immunotherapy but also for its potential application in studying other cell-cell interactions.

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.

A hybrid demultiplexing strategy that improves performance and robustness of cell hashing

Cell hashing, a nucleotide barcode-based method that allows users to pool multiple samples and demultiplex in downstream analysis, has gained widespread popularity in single-cell sequencing due to its compatibility, simplicity, and cost-effectiveness. Despite these advantages, the performance of this method remains unsatisfactory under certain circumstances, especially in experiments that have imbalanced sample sizes or use many hashtag antibodies. Here, we introduce a hybrid demultiplexing strategy that increases accuracy and cell recovery in multi-sample single-cell experiments. This approach correlates the results of cell hashing and genetic variant clustering, enabling precise and efficient cell identity determination without additional experimental costs or efforts. In addition, we developed HTOreader, a demultiplexing tool for cell hashing that improves the accuracy of cut-off calling by avoiding the dominance of negative signals in experiments with many hashtags or imbalanced sample sizes. When compared to existing methods using real-world datasets, this hybrid approach and HTOreader consistently generate reliable results with increased accuracy and cell recovery.

A risk-reward examination of sample multiplexing reagents for single cell RNA-Seq

Single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for understanding cellular heterogeneity and function. However the choice of sample multiplexing reagents can impact data quality and experimental outcomes. In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, across diverse sample types such as human peripheral blood mononuclear cells (PBMCs), mouse embryonic brain and patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We compared multiple demultiplexing algorithms which differed in performance depending on data quality. We find that minor improvements to laboratory workflows such as titration and rapid processing are critical to optimal performance. We also compared the performance of fixed scRNA-Seq kits and highlight the advantages of the Parse Biosciences kit for fragile samples. Highly multiplexed scRNA-Seq experiments require more sequencing resources, therefore we evaluated CRISPR-based destruction of non-informative genes to enhance sequencing value. Our comprehensive analysis provides insights into the selection of appropriate sample multiplexing reagents and protocols for scRNA-Seq experiments, facilitating more accurate and cost-effective studies.

Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines

Human cancer cell lines have long served as tools for cancer research and drug discovery, but the presence and the source of intra-cell-line heterogeneity remain elusive. Here, we perform single-cell RNA-sequencing and ATAC-sequencing on 42 and 39 human cell lines, respectively, to illustrate both transcriptomic and epigenetic heterogeneity within individual cell lines. Our data reveal that transcriptomic heterogeneity is frequently observed in cancer cell lines of different tissue origins, often driven by multiple common transcriptional programs. Copy number variation, as well as epigenetic variation and extrachromosomal DNA distribution all contribute to the detected intra-cell-line heterogeneity. Using hypoxia treatment as an example, we demonstrate that transcriptomic heterogeneity could be reshaped by environmental stress. Overall, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell-line heterogeneity and its dynamics, which would serve as an important resource for future cancer cell line-based studies.

Creation of a High-Throughput Microfluidic Platform for Single-Cell Transcriptome Sequencing of Cell-Cell Interactions

Cell-cell interaction is one of the major modalities for transmitting information between cells and activating the effects of functional cells. However, the construction of high-throughput analysis technologies from cell omics focusing on the impact of interactions of functional cells on targets has been relatively unexplored. Here, they propose a droplet-based microfluidic platform for cell-cell interaction sequencing (c-c-seq) and screening in vitro to address this challenge. A class of interacting cells is pre-labeled using cell molecular tags, and additional single-cell sequencing reagents are introduced to quickly form functional droplet mixes. Lastly, gene expression analysis is used to deduce the impact of the interaction, while molecular sequence tracing identifies the type of interaction. Research into the active effect between antigen-presenting cells and T cells, one of the most common cell-to-cell interactions, is crucial for the advancement of cancer therapy, particularly T cell receptor-engineered T cell therapy. As it allows for high throughput, this platform is superior to well plates as a research platform for cell-to-cell interactions. When combined with the next generation of sequencing, the platform may be able to more accurately evaluate interactions between epitopes and receptors and verify their functional relevance.

An optimized approach for multiplexing single-nuclear ATAC-seq using oligonucleotide-conjugated antibodies

BACKGROUND

Single-cell technologies to analyze transcription and chromatin structure have been widely used in many research areas to reveal the functions and molecular properties of cells at single-cell resolution. Sample multiplexing techniques are valuable when performing single-cell analysis, reducing technical variation and permitting cost efficiencies. Several commercially available methods have been used in many scRNA-seq studies. On the other hand, while several methods have been published, multiplexing techniques for single nuclear assay for transposase-accessible chromatin (snATAC)-seq assays remain under development. We developed a simple nucleus hashing method using oligonucleotide-conjugated antibodies recognizing nuclear pore complex proteins, NuHash, to perform snATAC-seq library preparations by multiplexing.

RESULTS

We performed multiplexing snATAC-seq analyses on a mixture of human and mouse cell samples (two samples, 2-plex, and four samples, 4-plex) using NuHash. The analyses on nuclei with at least 10,000 read counts showed that the demultiplexing accuracy of NuHash was high, and only ten out of 9144 nuclei (2-plex) and 150 of 12,208 nuclei (4-plex) had discordant classifications between NuHash demultiplexing and discrimination using reference genome alignments. The differential open chromatin region (OCR) analysis between female and male samples revealed that male-specific OCRs were enriched in chromosome Y (four out of nine). We also found that five female-specific OCRs (20 OCRs) were on chromosome X. A comparative analysis between snATAC-seq and deeply sequenced bulk ATAC-seq on the same samples revealed that the bulk ATAC-seq signal intensity was positively correlated with the number of cell clusters detected in snATAC-seq. Moreover, when we categorized snATAC-seq peaks based on the number of cell clusters in which the peak was present, we observed different distributions over different genomic features between the groups. This result suggests that the peak intensities of bulk ATAC-seq can be used to identify different types of functional loci.

CONCLUSIONS

Our multiplexing method using oligo-conjugated anti-nuclear pore complex proteins, NuHash, permits high-accuracy demultiplexing of samples. The NuHash protocol is straightforward, works on frozen samples, and requires no modifications for snATAC-seq library preparation.

Single-Cell Molecular Barcoding to Decode Multimodal Information Defining Cell States

Single-cell research has provided a breakthrough in biology to understand heterogeneous cell groups, such as tissues and organs, in development and disease. Molecular barcoding and subsequent sequencing technology insert a singlecell barcode into isolated single cells, allowing separation cell by cell. Given that multimodal information from a cell defines precise cellular states, recent technical advances in methods focus on simultaneously extracting multimodal data recorded in different biological materials (DNA, RNA, protein, etc.). This review summarizes recently developed singlecell multiomics approaches regarding genome, epigenome, and protein profiles with the transcriptome. In particular, we focus on how to anchor or tag molecules from a cell, improve throughputs with sample multiplexing, and record lineages, and we further discuss the future developments of the technology.

Sample-multiplexing approaches for single-cell sequencing

Single-cell sequencing is widely used in biological and medical studies. However, its application with multiple samples is hindered by inefficient sample processing, high experimental costs, ambiguous identification of true single cells, and technical batch effects. Here, we introduce sample-multiplexing approaches for single-cell sequencing in transcriptomics, epigenomics, genomics, and multiomics. In single-cell transcriptomics, sample multiplexing uses variants of native or artificial features as sample markers, enabling sample pooling and decoding. Such features include: (1) natural genetic variation, (2) nucleotide-barcode anchoring on cellular or nuclear membranes, (3) nucleotide-barcode internalization to the cytoplasm or nucleus, (4) vector-based barcode expression in cells, and (5) nucleotide-barcode incorporation during library construction. Other single-cell omics methods are based on similar concepts, particularly single-cell combinatorial indexing. These methods overcome current challenges, while enabling super-loading of single cells. Finally, selection guidelines are presented that can accelerate technological application.

scGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data

SUMMARY

Quantitative tools are needed to leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and integrate it with other single-cell data modalities. We present single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data. scGAD enables summarization at the gene unit while accounting for inherent gene-level genomic biases. Low-dimensional projections with scGAD capture clustering of cells based on their 3D structures. Significant chromatin interactions within and between cell types can be identified with scGAD. We further show that scGAD facilitates the integration of scHi-C data with other single-cell data modalities by enabling its projection onto reference low-dimensional embeddings. This multi-modal data integration provides an automated and refined cell-type annotation for scHi-C data.

AVAILABILITY AND IMPLEMENTATION

scGAD is part of the BandNorm R package at https://sshen82.github.io/BandNorm/articles/scGAD-tutorial.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

BFF and cellhashR: analysis tools for accurate demultiplexing of cell hashing data

MOTIVATION

Single-cell sequencing methods provide previously impossible resolution into the transcriptome of individual cells. Cell hashing reduces single-cell sequencing costs by increasing capacity on droplet-based platforms. Cell hashing methods rely on demultiplexing algorithms to accurately classify droplets; however, assumptions underlying these algorithms limit accuracy of demultiplexing, ultimately impacting the quality of single-cell sequencing analyses.

RESULTS

We present Bimodal Flexible Fitting (BFF) demultiplexing algorithms BFFcluster and BFFraw, a novel class of algorithms that rely on the single inviolable assumption that barcode count distributions are bimodal. We integrated these and other algorithms into cellhashR, a new R package that provides integrated QC and a single command to execute and compare multiple demultiplexing algorithms. We demonstrate that BFFcluster demultiplexing is both tunable and insensitive to issues with poorly behaved data that can confound other algorithms. Using two well-characterized reference datasets, we demonstrate that demultiplexing with BFF algorithms is accurate and consistent for both well-behaved and poorly behaved input data.

AVAILABILITY AND IMPLEMENTATION

cellhashR is available as an R package at https://github.com/BimberLab/cellhashR. cellhashR version 1.0.3 was used for the analyses in this manuscript and is archived on Zenodo at https://www.doi.org/10.5281/zenodo.6402477.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell genomics in plants: current state, future directions, and hurdles to overcome

Single-cell genomics has the potential to revolutionize the study of plant development and tissue-specific responses to environmental stimuli by revealing heretofore unknown players and gene regulatory processes. Here, I focus on the current state of single-cell genomics in plants, emerging technologies and applications, in addition to outlining possible future directions for experiments. I describe approaches to enable cheaper and larger experiments and technologies to measure multiple types of molecules to better model and understand cell types and their different states and trajectories throughout development. Lastly, I discuss the inherent limitations of single-cell studies and the technological hurdles that need to be overcome to widely apply single-cell genomics in crops to generate the greatest possible knowledge gain.

Improved ClickTags Enable Live-cell Barcoding for Highly Multiplexed Single-cell Sequencing

Click chemistry-enabled DNA barcoding of cells provides a universal strategy for sample multiplexing in single-cell RNA-seq (scRNA-seq). However, current ClickTags are limited to fixed samples as they only label cells efficiently in methanol. Herein, we report the development of a new protocol for barcoding live cells with improved ClickTags. The optimized reactions barcoded live cells without perturbing their physiological states, which allowed sample multiplexing of live cells in scRNA-seq. The general applicability of this protocol is demonstrated in diversified types of samples, including murine and human primary samples. Up to 16 samples across these two species are successfully multiplexed and demultiplexed with high consistency. The wide applications of this method could help to increase throughput, reduce cost and remove the batch effect in scRNA-seq, which is especially valuable for studying clinical samples from a large cohort.

A versatile and highly reproducible approach for live cell sample multiplexing is achieved by DNA barcoding via “click chemistry” in single-cell RNA-seq.