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Background One of the technical limits of droplet-based single-cell RNA sequencing (scRNA-seq) is the presence of multiplets, i.e. droplets that capture multiple cells. Sample-multiplexing scRNA-seq (mx-scRNA-seq) enables us to evaluate large numbers of different samples or experiments simultaneously by reducing the occurrence of undetectable multiplets. However, there is still a possibility of hidden multiplets among what appear to be singlets, for which we introduce the term stealth multiplets, and their probability is yet to be quantitatively examined. Results We developed a simple theoretical model to predict four classes of possible multiplets in mx-scRNA-seq: Homogeneous stealth, partial stealth, multilabelled, and unlabelled. We estimated the probability of each class and have found that the partial stealth multiplet, which has been previously overlooked, may impact the results of the whole dataset, particularly when the labelling process or demultiplexing is suboptimal. Also, we demonstrated their presence in real mx-scRNA-seq datasets both in oligonucleotide-barcode demultiplexing and genotype-based demultiplexing. Conclusion Our results show the importance of optimising the labelling procedure and choosing the most suitable demultiplexing algorithm. We thus offer a theoretical basis to estimate the probability of each type of multiplet to ensure the integrity of mx-scRNA-seq.

Single-cell RNA sequencing methods focusing on the 5′-end of transcripts can reveal promoter and enhancer activity and efficiently profile immune receptor repertoire. However, ultra-high-throughput 5′-end single-cell RNA sequencing methods have not been described. We introduce FIPRESCI, 5′-end single-cell combinatorial indexing RNA-Seq, enabling massive sample multiplexing and increasing the throughput of the droplet microfluidics system by over tenfold. We demonstrate FIPRESCI enables the generation of approximately 100,000 single-cell transcriptomes from E10.5 whole mouse embryos in a single-channel experiment, and simultaneous identification of subpopulation differences and T cell receptor signatures of peripheral blood T cells from 12 cancer patients.

Sample multiplexing facilitates single‐cell sequencing by reducing costs, revealing subtle difference between similar samples, and identifying artifacts such as cell doublets. However, universal and cost‐effective strategies are rather limited. Here, we reported a concanavalin A‐based sample barcoding strategy (CASB), which could be followed by both single‐cell mRNA and ATAC (assay for transposase‐accessible chromatin) sequencing techniques. The method involves minimal sample processing, thereby preserving intact transcriptomic or epigenomic patterns. We demonstrated its high labeling efficiency, high accuracy in assigning cells/nuclei to samples regardless of cell type and genetic background, and high sensitivity in detecting doublets by three applications: 1) CASB followed by scRNA‐seq to track the transcriptomic dynamics of a cancer cell line perturbed by multiple drugs, which revealed compound‐specific heterogeneous response; 2) CASB together with both snATAC‐seq and scRNA‐seq to illustrate the IFN‐γ‐mediated dynamic changes on epigenome and transcriptome profile, which identified the transcription factor underlying heterogeneous IFN‐γ response; and 3) combinatorial indexing by CASB, which demonstrated its high scalability. Synopsis CASB is a new method for sample multiplexing compatible with both single‐cell RNA‐seq and ATAC‐seq. CASB is highly accurate, efficient and sensitive and allows more diverse types of information to be incorporated in single‐cell sequencing experiments. CASB is the first single‐cell/nucleus sample multiplexing strategy compatible with both scRNA‐seq & snATAC‐seq. CASB allows both simultaneous combinatorial barcoding and sequential split‐pool barcoding. While being highly efficient, accurate and sensitive, CASB is experiment‐friendly and cost‐effective. Graphical Abstract CASB is a new method for sample multiplexing compatible with both single‐cell RNA‐seq and ATAC‐seq. CASB is highly accurate, efficient and sensitive and allows more diverse types of information to be incorporated in single‐cell sequencing experiments.

Background Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called “hashing.” Results Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. We also compare TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects. Conclusions Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. However, antibodies demonstrate better results on tissues like spleen or lung.

Sample multiplexing enables pooled analysis during single-cell RNA sequencing workflows, thereby increasing throughput and reducing batch effects. A challenge for all multiplexing techniques is to link sample-specific barcodes with cell-specific barcodes, then demultiplex sample identity post-sequencing. However, existing demultiplexing tools fail under many real-world conditions where barcode cross-contamination is an issue. We therefore developed deMULTIplex2, an algorithm inspired by a mechanistic model of barcode cross-contamination. deMULTIplex2 employs generalized linear models and expectation–maximization to probabilistically determine the sample identity of each cell. Benchmarking reveals superior performance across various experimental conditions, particularly on large or noisy datasets with unbalanced sample compositions.

Background Single-cell RNA sequencing (scRNA-seq) provides high-dimensional measurements of transcript counts in individual cells. However, high assay costs and artifacts associated with analyzing samples across multiple sequencing runs limit the study of large numbers of samples. Sample multiplexing technologies such as MULTI-seq and antibody hashing using single-cell multiplexing kit (SCMK) reagents (BD Biosciences) use sample-specific sequence tags to enable individual samples to be sequenced in a pooled format, markedly lowering per-sample processing and sequencing costs while minimizing technical artifacts. Critically, however, pooling samples could introduce new artifacts, partially negating the benefits of sample multiplexing. In particular, no study to date has evaluated whether pooling peripheral blood mononuclear cells (PBMCs) from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures. Results Here, we applied the 10x Genomics scRNA-seq platform to MULTI-seq and/or SCMK-labeled PBMCs from a single donor with and without pooling with PBMCs from unrelated donors for 30 min at 4 °C. We did not detect any alloreactivity signal between mixed and unmixed PBMCs across a variety of metrics, including alloreactivity marker gene expression in CD4+ T cells, cell type proportion shifts, and global gene expression profile comparisons using Gene Set Enrichment Analysis and Jensen-Shannon Divergence. These results were additionally mirrored in publicly-available scRNA-seq data generated using a similar experimental design. Moreover, we identified confounding gene expression signatures linked to PBMC preparation method (e.g., Trima apheresis), as well as SCMK sample classification biases against activated CD4+ T cells which were recapitulated in two other SCMK-incorporating scRNA-seq datasets. Conclusions We demonstrate that (i) mixing PBMCs from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) does not cause an allogeneic response, and (ii) that Trima apheresis and PBMC sample multiplexing using SCMK reagents can introduce undesirable technical artifacts into scRNA-seq data. Collectively, these observations establish important benchmarks for future cross-sectional immunological scRNA-seq experiments.

Barcoding technology has greatly improved the throughput of cells and genes detected in single‐cell RNA sequencing (scRNA‐seq) studies. Recently, increasing studies have paid more attention to the use of this technology to increase the throughput of samples, as it has greatly reduced the processing time, technical batch effects, and library preparation costs, and lowered the per‐sample cost. In this review, the various DNA‐based barcoding methods for sample multiplexing are focused on, specifically, on the four major barcoding strategies. A detailed comparison of the barcoding methods is also presented, focusing on aspects such as sample/cell throughput and gene detection, and guidelines for choosing the most appropriate barcoding technique according to the personalized requirements are developed. Finally, the critical applications of sample multiplexing and technical challenges in combinatorial labeling, barcoding in vivo, and multimodal tagging at the spatially resolved resolution, as well as, the future prospects of multiplexed scRNA‐seq, for example, prioritizing and predicting the severity of coronavirus disease 2019 (COVID‐19) in patients of different gender and age are highlighted. DNA‐based barcoding technology enables simultaneous large‐scale sample multiplexing for single‐cell RNA sequencing (scRNA‐seq) and greatly reduces technical batch effects which make it an unparalleled performance in high‐throughput perturbation screening and tracking the dynamic process of cell differentiation. The breakthroughs of combinatorial labelling, barcoding in vivo, and multimodal barcoding at the spatially resolved resolution make more potential applications in life sciences possible.

Panax ginseng (PG) and Panax notoginseng (PN) are highly valuable Chinese medicines (CM). Although both CMs have similar active constituents, their clinical applications are clearly different. Over the past decade, RNA sequencing (RNA-seq) analysis has been employed to investigate the molecular mechanisms of extracts or monomers. However, owing to the limited number of samples in standard RNA-seq, few studies have systematically compared the effects of PG and PN spanning multiple conditions at the transcriptomic level. Here, we developed an approach that simultaneously profiles transcriptome changes for multiplexed samples using RNA-seq (TCM-seq), a high-throughput, low-cost workflow to molecularly evaluate CM perturbations. A species-mixing experiment was conducted to illustrate the accuracy of sample multiplexing in TCM-seq. Transcriptomes from repeated samples were used to verify the robustness of TCM-seq. We then focused on the primary active components, Panax notoginseng saponins (PNS) and Panax ginseng saponins (PGS) extracted from PN and PG, respectively. We also characterized the transcriptome changes of 10 cell lines, treated with four different doses of PNS and PGS, using TCM-seq to compare the differences in their perturbing effects on genes, functional pathways, gene modules, and molecular networks. The results of transcriptional data analysis showed that the transcriptional patterns of various cell lines were significantly distinct. PGS exhibited a stronger regulatory effect on genes involved in cardiovascular disease, whereas PNS resulted in a greater coagulation effect on vascular endothelial cells. This study proposes a paradigm to comprehensively explore the differences in mechanisms of action between CMs based on transcriptome readouts. [Display omitted] •A low-cost workflow of high-throughput transcriptional profiling was developed.•The atlas of signatures perturbed by PGS and PNS was established using TCM-seq.•The regulation of PGS and PNS was holistically compared with network analysis.•A paradigm of molecularly evaluating CMs with transcriptome readouts was proposed.

Single‐cell sequencing is revolutionizing biological research by enabling unprecedented cellular resolution, yet traditional multi‐sample experiments are often constrained by high costs and batch effects. Sample multiplexing offers a critical solution by uniquely tagging individual cells from diverse samples for pooled sequencing, thereby dramatically boosting throughput and improving data reliability by minimizing technical variability. This review provides a comprehensive and integrated perspective on the rapidly evolving field of single‐cell multiplexing. Major experimental strategies and the critical computational algorithms required for accurate sample deconvolution are surveyed, highlighting the crucial link between experimental design and computational accuracy. Furthermore, the diverse applications of these technologies in large‐scale clinical cohorts, multi‐omics integration, developmental biology, and high‐throughput drug screening are summarized. This review serves as an essential guide for researchers, empowering them to select the most appropriate methods to accelerate discoveries in disease mechanisms, therapeutic responses, and developmental biology. Sample multiplexing offers a critical solution for boosting throughput and minimizing batch effects in single‐cell sequencing. This review navigates the latest experimental methodologies and computational deconvolution algorithms, bridging the gap between wet‐lab design and analytical success. By summarizing cutting‐edge applications and offering practical guidance for method selection, it empowers researchers to optimize their experimental designs of single‐cell studies.