CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics

Enhancing CAR Macrophage Efferocytosis Via Surface Engineered Lipid Nanoparticles Targeting LXR Signaling

The removal of dying cells, or efferocytosis, is an indispensable part of resolving inflammation. However, the inflammatory microenvironment of the atherosclerotic plaque frequently affects the biology of both apoptotic cells and resident phagocytes, rendering efferocytosis dysfunctional. To overcome this problem, a chimeric antigen receptor (CAR) macrophage that can target and engulf phagocytosis-resistant apoptotic cells expressing CD47 is developed. In both normal and inflammatory circumstances, CAR macrophages exhibit activity equivalent to antibody blockage. The surface of CAR macrophages is modified with reactive oxygen species (ROS)-responsive therapeutic nanoparticles targeting the liver X receptor pathway to improve their cell effector activities. The combination of CAR and nanoparticle engineering activated lipid efflux pumps enhances cell debris clearance and reduces inflammation. It is further suggested that the undifferentiated CAR-Ms can transmigrate within a mico-fabricated vessel system. It is also shown that our CAR macrophage can act as a chimeric switch receptor (CSR) to withstand the immunosuppressive inflammatory environment. The developed platform has the potential to contribute to the advancement of next-generation cardiovascular disease therapies and further studies include in vivo experiments.

Sample multiplexing for retinal single-cell RNA-sequencing

Rare cell populations can be challenging to characterize using microfluidic single-cell RNA sequencing (scRNA-seq) platforms. Typically, the population of interest must be enriched and pooled from multiple biological specimens for efficient collection. However, these practices preclude the resolution of sample origin together with phenotypic data and are problematic in experiments in which biological or technical variation is expected to be high (e.g., disease models, genetic perturbation screens, or human samples). One solution is sample multiplexing whereby each sample is tagged with a unique sequence barcode that is resolved bioinformatically. We have established a scRNA-seq sample multiplexing pipeline for mouse retinal ganglion cells using cholesterol-modified-oligos and utilized the enhanced precision to investigate cell type distribution and transcriptomic variance across retinal samples. As single cell transcriptomics are becoming more widely used to research development and disease, sample multiplexing represents a useful method to enhance the precision of scRNA-seq analysis.

The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy

In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.

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 Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

Motivation

Recently, single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have provided key insights into cancer heterogeneity. Scaling these technologies across large patient cohorts, however, is cost and time prohibitive. Multiplexing, in which cells from unique patients are pooled into a single experiment, offers a possible solution. While multiplexing methods exist for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need.

Results

Here, we introduce SNACS: Single-Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting. SNACS relies on a combination of patient-level cell-surface identifiers and natural variation in genetic polymorphisms to demultiplex scDNAseq data. We demonstrated the performance of SNACS on a dataset consisting of multi-sample experiments from patients with leukemia where we knew truth from single-sample experiments from the same patients. Using SNACS, accuracy ranged from 0.948 - 0.991 vs 0.552 - 0.934 using demultiplexing methods from the single-cell literature.

deMULTIplex2: robust sample demultiplexing for scRNA-seq

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.

The emerging roles of deep crypt secretory cells in colonic physiology

Deep crypt secretory (DCS) cells are a population of epithelial cells located at the colonic crypt base that share some similarities to Paneth and goblet cells. They were initially defined as c-Kit expressing cells, though subsequent work showed that they are more specifically marked by Reg4 in the murine colon. The best-understood function of DCS cells at present is supporting the stem cell niche by generating Notch and EGF ligands. However, as these cells also express immunoregulatory (e.g., Ccl6) and host defense (e.g., Retnlb) genes, it is likely they have additional functions in maintaining colonic health outside of maintenance of the stem niche. Recent advances in single-cell transcriptomic profiling hint at additional epithelial and immune roles that may exist for these cells and have aided in elucidating their developmental lineage. This review highlights the emerging evidence supporting a crucial role for DCS cells in intestinal physiology, the current understanding of how these cells are regulated, and their potential role(s) in colonic disease.

Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes

Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage-tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics. However, reliance on transcriptional profiling limits adaptation to other single-cell assays. With CellTag-multi, we present an approach that enables direct capture of heritable random barcodes expressed as polyadenylated transcripts, in both single-cell RNA sequencing and single-cell Assay for Transposase Accessible Chromatin using sequencing assays, allowing for independent clonal tracking of transcriptional and epigenomic cell states. We validate CellTag-multi to characterize progenitor cell lineage priming during mouse hematopoiesis. Additionally, in direct reprogramming of fibroblasts to endoderm progenitors, we identify core regulatory programs underlying on-target and off-target fates. Furthermore, we reveal the transcription factor Zfp281 as a regulator of reprogramming outcome, biasing cells toward an off-target mesenchymal fate. Our results establish CellTag-multi as a lineage-tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.

Single-cell lineage tracing reveals hierarchy and mechanism of adipocyte precursor maturation

White adipose tissue is crucial in various physiological processes. In response to high caloric intake, adipose tissue may expand by generating new adipocytes. Adipocyte precursor cells (progenitors and preadipocytes) are essential for generating mature adipocytes, and single-cell RNA sequencing provides new means to identify these populations. Here, we characterized adipocyte precursor populations in the skin, an adipose depot with rapid and robust generation of mature adipocytes. We identified a new population of immature preadipocytes, revealed a biased differentiation potential of progenitor cells, and identified Sox9 as a critical factor in driving progenitors toward adipose commitment, the first known mechanism of progenitor differentiation. These findings shed light on the specific dynamics and molecular mechanisms underlying rapid adipogenesis in the skin.

FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5'-end single-cell RNA sequencing

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.

Microscale combinatorial stimulation of human myeloid cells reveals inflammatory priming by viral ligands

Cells sense a wide variety of signals and respond by adopting complex transcriptional states. Most single-cell profiling is carried out today at cellular baseline, blind to cells' potential spectrum of functional responses. Exploring the space of cellular responses experimentally requires access to a large combinatorial perturbation space. Single-cell genomics coupled with multiplexing techniques provide a useful tool for characterizing cell states across several experimental conditions. However, current multiplexing strategies require programmatic handling of many samples in macroscale arrayed formats, precluding their application in large-scale combinatorial analysis. Here, we introduce StimDrop, a method that combines antibody-based cell barcoding with parallel droplet processing to automatically formulate cell population × stimulus combinations in a microfluidic device. We applied StimDrop to profile the effects of 512 sequential stimulation conditions on human dendritic cells. Our results demonstrate that priming with viral ligands potentiates hyperinflammatory responses to a second stimulus, and show transcriptional signatures consistent with this phenomenon in myeloid cells of patients with severe COVID-19.

Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics

Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.

Gene regulatory network reconfiguration in direct lineage reprogramming

In direct lineage conversion, transcription factor (TF) overexpression reconfigures gene regulatory networks (GRNs) to reprogram cell identity. We previously developed CellOracle, a computational method to infer GRNs from single-cell transcriptome and epigenome data. Using inferred GRNs, CellOracle simulates gene expression changes in response to TF perturbation, enabling in silico interrogation of network reconfiguration. Here, we combine CellOracle analysis with lineage tracing of fibroblast to induced endoderm progenitor (iEP) conversion, a prototypical direct reprogramming paradigm. By linking early network state to reprogramming outcome, we reveal distinct network configurations underlying successful and failed fate conversion. Via in silico simulation of TF perturbation, we identify new factors to coax cells into successfully converting their identity, uncovering a central role for the AP-1 subunit Fos with the Hippo signaling effector, Yap1. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations, providing new mechanistic insights into lineage reprogramming.

Multiplexed Single-Nucleus RNA Sequencing Using Lipid-Oligo Barcodes

This protocol describes a robust pipeline for simultaneously analyzing multiple samples by single-nucleus (sn)RNA-seq. cDNA obtained from each single sample are labeled with the same lipid-coupled oligonucleotide barcode (10X Genomics). Nuclei from as many as 12 individual samples can be pooled together and simultaneously processed for cDNA library construction and subsequent DNA sequencing. While previous protocols using lipid-coupled oligonucleotide barcodes were optimized for analysis of samples consisting of viable cells, this protocol is optimized for analyses of quick-frozen cell samples. The protocol ensures efficient recovery of nuclei both by incorporating high sucrose buffered solutions and by including a tracking dye (trypan blue) during nuclei isolation. The protocol also describes a procedure for removing single nuclei 'artifacts' by removing cell debris prior to single nuclear fractionation. This protocol informs the use of computational tools for filtering poorly labeled nuclei and assigning sample identity using barcode unique molecular identifier (UMI) read counts percentages. The computational pipeline is applicable to either cultured or primary, fresh or frozen cells, regardless of their cell types and species. Overall, this protocol reduces batch effects and experimental costs while enhancing sample comparison. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Labeling cells with lipid oligo barcodes and generating multiplexed single-nucleus RNA-seq libraries Basic Protocol 2: Bioinformatic deconvolution of the multiplexed snRNAseq libraries.

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.

Diverse partial reprogramming strategies restore youthful gene expression and transiently suppress cell identity

Partial pluripotent reprogramming can reverse features of aging in mammalian cells, but the impact on somatic identity and the necessity of individual reprogramming factors remain unknown. Here, we used single-cell genomics to map the identity trajectory induced by partial reprogramming in multiple murine cell types and dissected the influence of each factor by screening all Yamanaka Factor subsets with pooled single-cell screens. We found that partial reprogramming restored youthful expression in adipogenic and mesenchymal stem cells but also temporarily suppressed somatic identity programs. Our pooled screens revealed that many subsets of the Yamanaka Factors both restore youthful expression and suppress somatic identity, but these effects were not tightly entangled. We also found that a multipotent reprogramming strategy inspired by amphibian regeneration restored youthful expression in myogenic cells. Our results suggest that various sets of reprogramming factors can restore youthful expression with varying degrees of somatic identity suppression. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Clonal barcoding with qPCR detection enables live cell functional analyses for cancer research

Single-cell analysis methods are valuable tools; however, current approaches do not easily enable live cell retrieval. That is a particular issue when further study of cells that were eliminated during experimentation could provide critical information. We report a clonal molecular barcoding method, called SunCatcher, that enables longitudinal tracking and live cell functional analysis. From complex cell populations, we generate single cell-derived clonal populations, infect each with a unique molecular barcode, and retain stocks of individual barcoded clones (BCs). We develop quantitative PCR-based and next-generation sequencing methods that we employ to identify and quantify BCs in vitro and in vivo. We apply SunCatcher to various breast cancer cell lines and combine respective BCs to create versions of the original cell lines. While the heterogeneous BC pools reproduce their original parental cell line proliferation and tumor progression rates, individual BCs are phenotypically and functionally diverse. Early spontaneous metastases can also be identified and quantified. SunCatcher thus provides a rapid and sensitive approach for studying live single-cell clones and clonal evolution, and performing functional analyses.

Universal Surface Biotinylation: a simple, versatile and cost-effective sample multiplexing method for single-cell RNA-seq analysis

Recent advances in single-cell analysis technology have made it possible to analyse tens of thousands of cells at a time. In addition, sample multiplexing techniques, which allow the analysis of several types of samples in a single run, are very useful for reducing experimental costs and improving experimental accuracy. However, a problem with this technique is that antigens and antibodies for universal labelling of various cell types may not be fully available. To overcome this issue, we developed a universal labelling technique, Universal Surface Biotinylation (USB), which does not depend on specific cell surface proteins. By introducing biotin into the amine group of any cell surface protein, we have obtained good labelling results in all the cell types we have tested. Combining with DNA-tagged streptavidin, it is possible to label each cell sample with specific DNA ‘hashtag’. Compared with the conventional cell hashing method, the USB procedure seemed to have no discernible adverse effect on the acquisition of the transcriptome in each cell, according to the model experiments using differentiating mouse embryonic stem cells. This method can be theoretically used for any type of cells, including cells to which the conventional cell hashing method has not been applied successfully.

Visual barcodes for clonal-multiplexing of live microscopy-based assays

While multiplexing samples using DNA barcoding revolutionized the pace of biomedical discovery, multiplexing of live imaging-based applications has been limited by the number of fluorescent proteins that can be deconvoluted using common microscopy equipment. To address this limitation, we develop visual barcodes that discriminate the clonal identity of single cells by different fluorescent proteins that are targeted to specific subcellular locations. We demonstrate that deconvolution of these barcodes is highly accurate and robust to many cellular perturbations. We then use visual barcodes to generate ‘Signalome’ cell-lines by mixing 12 clones of different live reporters into a single population, allowing simultaneous monitoring of the activity in 12 branches of signaling, at clonal resolution, over time. Using the ‘Signalome’ we identify two distinct clusters of signaling pathways that balance growth and proliferation, emphasizing the importance of growth homeostasis as a central organizing principle in cancer signaling. The ability to multiplex samples in live imaging applications, both in vitro and in vivo may allow better high-content characterization of complex biological systems.

Multiplex analyses of samples allow understanding complex processes in cancer initiation, progression and therapy response. Here, the authors present a fluorescence imaging-based visual barcode for livecell clonal-multiplexing which allows identifying signalling pathways clusters in response to different chemotherapy compounds.

Functionalized Lineage Tracing Can Enable the Development of Homogenization-Based Therapeutic Strategies in Cancer

The therapeutic landscape across many cancers has dramatically improved since the introduction of potent targeted agents and immunotherapy. Nonetheless, success of these approaches is too often challenged by the emergence of therapeutic resistance, fueled by intratumoral heterogeneity and the immense evolutionary capacity inherent to cancers. To date, therapeutic strategies have attempted to outpace the evolutionary tempo of cancer but frequently fail, resulting in lack of tumor response and/or relapse. This realization motivates the development of novel therapeutic approaches which constrain evolutionary capacity by reducing the degree of intratumoral heterogeneity prior to treatment. Systematic development of such approaches first requires the ability to comprehensively characterize heterogeneous populations over the course of a perturbation, such as cancer treatment. Within this context, recent advances in functionalized lineage tracing approaches now afford the opportunity to efficiently measure multimodal features of clones within a tumor at single cell resolution, enabling the linkage of these features to clonal fitness over the course of tumor progression and treatment. Collectively, these measurements provide insights into the dynamic and heterogeneous nature of tumors and can thus guide the design of homogenization strategies which aim to funnel heterogeneous cancer cells into known, targetable phenotypic states. We anticipate the development of homogenization therapeutic strategies to better allow for cancer eradication and improved clinical outcomes.

Capybara: A computational tool to measure cell identity and fate transitions

Measuring cell identity in development, disease, and reprogramming is challenging as cell types and states are in continual transition. Here, we present Capybara, a computational tool to classify discrete cell identity and intermediate "hybrid" cell states, supporting a metric to quantify cell fate transition dynamics. We validate hybrid cells using experimental lineage tracing data to demonstrate the multi-lineage potential of these intermediate cell states. We apply Capybara to diagnose shortcomings in several cell engineering protocols, identifying hybrid states in cardiac reprogramming and off-target identities in motor neuron programming, which we alleviate by adding exogenous signaling factors. Further, we establish a putative in vivo correlate for induced endoderm progenitors. Together, these results showcase the utility of Capybara to dissect cell identity and fate transitions, prioritizing interventions to enhance the efficiency and fidelity of stem cell engineering.

Re-building limbs, one cell at a time

New techniques for visualizing and interrogating single cells hold the key to unlocking the underlying mechanisms of salamander limb regeneration.

Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

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.

Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches

Background:

Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference.

Methods:

We utilized datasets with temporal single-cell transcriptomics profiles from public repositories. Combining multiple methods at each level of the workflow, we have performed over 6 k analysis and evaluated the results of clustering and pseudotime estimation using adjusted rand index and rank correlation metrics. We have further integrated neural network methods to assess whether models with increased complexity can show increased bias/variance trade-off.

Results:

Combinatorial workflows showed that utilizing non-linear dimension reduction techniques such as t-SNE and UMAP are sensitive to initial preprocessing steps hence clustering results on dimension reduced space of single-cell datasets should be utilized carefully. Similarly, pseudotime estimation methods that depend on previous non-linear dimension reduction steps can result in highly variable trajectories. In contrast, methods that avoid non-linearity such as WOT can result in repeatable inferences of temporal gene expression dynamics. Furthermore, imputation methods do not improve clustering or trajectory inference results substantially in terms of repeatability. In contrast, the selection of the normalization method shows an increased effect on downstream analysis where ScTransform reduces variability overall.

Recapitulating human myogenesis ex vivo using human pluripotent stem cells

Human pluripotent stem cells (hPSCs) provide a human model for developmental myogenesis, disease modeling and development of therapeutics. Differentiation of hPSCs into muscle stem cells has the potential to provide a cell-based therapy for many skeletal muscle wasting diseases. This review describes the current state of hPSCs towards recapitulating human myogenesis ex vivo, considerations of stem cell and progenitor cell state as well as function for future use of hPSC-derived muscle cells in regenerative medicine.

Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts across Clytia medusa cell types

We present an organism-wide, transcriptomic cell atlas of the hydrozoan medusa Clytia hemisphaerica and describe how its component cell types respond to perturbation. Using multiplexed single-cell RNA sequencing, in which individual animals were indexed and pooled from control and perturbation conditions into a single sequencing run, we avoid artifacts from batch effects and are able to discern shifts in cell state in response to organismal perturbations. This work serves as a foundation for future studies of development, function, and regeneration in a genetically tractable jellyfish species. Moreover, we introduce a powerful workflow for high-resolution, whole-animal, multiplexed single-cell genomics that is readily adaptable to other traditional or nontraditional model organisms.

Simple oligonucleotide-based multiplexing of single-cell chromatin accessibility

Microdroplet single-cell ATAC-seq is widely used to measure chromatin accessibility, however, highly scalable and simple sample multiplexing procedures are not available. Here, we present a transposome-assisted single nucleus barcoding approach for ATAC-seq (SNuBar-ATAC) that utilizes a single oligonucleotide adaptor for multiplexing samples during the existing tagmentation step and does not require a pre-labeling procedure. The accuracy and scalability of SNuBar-ATAC was evaluated using cell line mixture experiments. We applied SNuBar-ATAC to investigate treatment-induced chromatin accessibility dynamics by multiplexing 28 mice with lung tumors that received different combinations of chemo, radiation, and targeted immunotherapy. We also applied SNuBar-ATAC to study spatial epigenetic heterogeneity by multiplexing 32 regions from a human breast tissue. Additionally, we show that SNuBar can multiplex single cell ATAC and RNA multiomic assays in cell lines and human breast tissue samples. Our data show that SNuBar is a highly accurate, easy-to-use, and scalable system for multiplexing scATAC-seq and scATAC and RNA co-assay experiments.

Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation

Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.

DNA barcode to trace the development and differentiation of cord blood stem cells (Review)

Umbilical cord blood transplantation was first reported in 1980. Since then, additional research has indicated that umbilical cord blood stem cells (UCBSCs) have various advantages, such as multi-lineage differentiation potential and potent renewal activity, which may be induced to promote their differentiation into a variety of seed cells for tissue engineering and the treatment of clinical and metabolic diseases. Recent studies suggested that UCBSCs are able to differentiate into nerve cells, chondrocytes, hepatocyte-like cells, fat cells and osteoblasts. The culture of UCBSCs has developed from feeder-layer to feeder-free culture systems. The classical techniques of cell labeling and tracing by gene transfection and fluorescent dye and nucleic acid analogs have evolved to DNA barcode technology mediated by transposon/retrovirus, cyclization recombination-recombinase and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 strategies. DNA barcoding for cell development tracing has advanced to include single cells and single nucleic acid mutations. In the present study, the latest research findings on the development and differentiation, culture techniques and labeling and tracing of UCBSCs are reviewed. The present study may increase the current understanding of UCBSC biology and its clinical applications.

POLYseq: A poly(β-amino ester)-based vector for multifunctional cellular barcoding

Despite evolving biological application of next-generation sequencing (NGS) at single-cell level, current techniques in NGS library preparation restrict multiplexing, necessitating the costly preparation of distinct libraries for each sample. Here, we report the development of a novel poly(β-amino) ester labeling system synthesized with inexpensive, common reagents, termed POLYseq, capable of efficiently delivering fluorescent molecules or sample-distinguishing DNA barcodes through non-covalent binding enabling rapid creation of custom sample pools. Chemical formulation was found to determine cellular labeling propensity. Live image-based tracking of fluorescent conjugated POLYseq vectors demonstrated lysosomal compartmentalization. Barcode labeling was uniformly detected across 90% of cells by single-cell RNA sequencing, allowing for the successful identification of human and mouse cultured cell lines from a single pool. These findings highlight the multifunctional applications of POLYseq in live cell imaging and NGS in a scalable and cost-effective manner.

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POLYseq using inexpensive, commercially available reagents in two-step procedure

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POLYseq efficiently binds fluorescent molecules and single-stranded DNA oligomers

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10 min to barcode cells in situ with excellent labeling retention

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POLYseq is amenable for droplet-based single-cell RNA sequencing

Applications of high-resolution clone tracking technologies in cancer

Tumors are comprised of dynamic, heterogenous cell populations characterized by numerous genetic and non-genetic alterations that accumulate and change with disease progression and treatment. Retrospective analyses of tumor evolution have relied on the measurement of genetic markers (such as copy number variants) to infer clonal dynamics. However, these approaches neglect the critical contributions of non-genetic drivers of disease. Techniques that harness the power of prospective clone tracking via heritable barcode tags provide an alternative strategy. In this review, we discuss methods for high-resolution, quantitative clone tracking, including recent advancements to pair barcode-specific functionality with scRNA-seq, clonal cell isolation, and in situ hybridization and imaging. We discuss these approaches in the context of cancer cell heterogeneity and treatment resistance.

Multiplexing Methods for Simultaneous Large-Scale Transcriptomic Profiling of Samples at Single-Cell Resolution

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.

Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

Cell atlas projects and high-throughput perturbation screens require single-cell sequencing at a scale that is challenging with current technology. To enable cost-effective single-cell sequencing for millions of individual cells, we developed 'single-cell combinatorial fluidic indexing' (scifi). The scifi-RNA-seq assay combines one-step combinatorial preindexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Preindexing allows us to load several cells per droplet and computationally demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared with multiround combinatorial indexing, scifi-RNA-seq provides an easy and efficient workflow. Compared to cell hashing methods, which flag and discard droplets containing more than one cell, scifi-RNA-seq resolves and retains individual transcriptomes from overloaded droplets. We benchmarked scifi-RNA-seq on various human and mouse cell lines, validated it for primary human T cells and applied it in a highly multiplexed CRISPR screen with single-cell transcriptome readout of T cell receptor activation.

All models are wrong, but some are useful: Establishing standards for stem cell-based embryo models

Detailed studies of the embryo allow an increasingly mechanistic understanding of development, which has proved of profound relevance to human disease. The last decade has seen in vitro cultured stem cell-based models of embryo development flourish, which provide an alternative to the embryo for accessible experimentation. However, the usefulness of any stem cell-based embryo model will be determined by how accurately it reflects in vivo embryonic development, and/or the extent to which it facilitates new discoveries. Stringent benchmarking of embryo models is thus an important consideration for this growing field. Here we provide an overview of means to evaluate both the properties of stem cells, the building blocks of most embryo models, as well as the usefulness of current and future in vitro embryo models.

Drivers of dynamic intratumor heterogeneity and phenotypic plasticity

Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and nongenetic variations among themselves-which is denoted as intratumor heterogeneity. Although some of these variations are inconsequential, others tend to contribute to cell state transition and phenotypic heterogeneity, providing a substrate for somatic evolution. Tumor cell phenotypes can dynamically change under the influence of genetic mutations, epigenetic modifications, and microenvironmental contexts. Although epigenetic and microenvironmental changes are adaptive, genetic mutations are usually considered permanent. Emerging reports suggest that certain classes of genetic alterations show extensive reversibility in tumors in clinically relevant timescales, contributing as major drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Dynamic heterogeneity and phenotypic plasticity can confer resistance to treatment, promote metastasis, and enhance evolvability in cancer. Here, we first highlight recent efforts to characterize intratumor heterogeneity at genetic, epigenetic, and microenvironmental levels. We then discuss phenotypic plasticity and cell state transition by tumor cells, under the influence of genetic and nongenetic determinants and their clinical significance in classification of tumors and therapeutic decision-making.

Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells

BACKGROUND

The discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) has provided a foundation for in vitro human disease modelling, drug development and population genetics studies. Gene expression plays a critical role in complex disease risk and therapeutic response. However, while the genetic background of reprogrammed cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells which would provide significant resolution. By integrating single cell RNA-sequencing (scRNA-seq) and population genetics, we apply a framework in which to evaluate cell type-specific effects of genetic variation on gene expression.

RESULTS

Here, we perform scRNA-seq on 64,018 fibroblasts from 79 donors and map expression quantitative trait loci (eQTLs) at the level of individual cell types. We demonstrate that the majority of eQTLs detected in fibroblasts are specific to an individual cell subtype. To address if the allelic effects on gene expression are maintained following cell reprogramming, we generate scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type-specific eQTLs in iPSCs and show that the eQTLs in fibroblasts almost entirely disappear during reprogramming.

CONCLUSIONS

This work provides an atlas of how genetic variation influences gene expression across cell subtypes and provides evidence for patterns of genetic architecture that lead to cell type-specific eQTL effects.

Phylodynamics for cell biologists

Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how "tree thinking" is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.

No detectable alloreactive transcriptional responses under standard sample preparation conditions during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells

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.

Defining the Teratoma as a Model for Multi-lineage Human Development

We propose that the teratoma, a recognized standard for validating pluripotency in stem cells, could be a promising platform for studying human developmental processes. Performing single-cell RNA sequencing (RNA-seq) of 179,632 cells across 23 teratomas from 4 cell lines, we found that teratomas reproducibly contain approximately 20 cell types across all 3 germ layers, that inter-teratoma cell type heterogeneity is comparable with organoid systems, and teratoma gut and brain cell types correspond well to similar fetal cell types. Furthermore, cellular barcoding confirmed that injected stem cells robustly engraft and contribute to all lineages. Using pooled CRISPR-Cas9 knockout screens, we showed that teratomas can enable simultaneous assaying of the effects of genetic perturbations across all germ layers. Additionally, we demonstrated that teratomas can be sculpted molecularly via microRNA (miRNA)-regulated suicide gene expression to enrich for specific tissues. Taken together, teratomas are a promising platform for modeling multi-lineage development, pan-tissue functional genetic screening, and tissue engineering.

Lineage tracing meets single-cell omics: opportunities and challenges

A fundamental goal of developmental and stem cell biology is to map the developmental history (ontogeny) of differentiated cell types. Recent advances in high-throughput single-cell sequencing technologies have enabled the construction of comprehensive transcriptional atlases of adult tissues and of developing embryos from measurements of up to millions of individual cells. Parallel advances in sequencing-based lineage-tracing methods now facilitate the mapping of clonal relationships onto these landscapes and enable detailed comparisons between molecular and mitotic histories. Here we review recent progress and challenges, as well as the opportunities that emerge when these two complementary representations of cellular history are synthesized into integrated models of cell differentiation.

CellTagging: combinatorial indexing to simultaneously map lineage and identity at single-cell resolution

Single-cell technologies are offering unparalleled insight into complex biology, revealing the behavior of rare cell populations that are masked in bulk population analyses. One current limitation of single-cell approaches is that lineage relationships are typically lost as a result of cell processing. We recently established a method, CellTagging, permitting the parallel capture of lineage information and cell identity via a combinatorial cell indexing approach. CellTagging integrates with high-throughput single-cell RNA sequencing, where sequential rounds of cell labeling enable the construction of multi-level lineage trees. Here, we provide a detailed protocol to (i) generate complex plasmid and lentivirus CellTag libraries for labeling of cells; (ii) sequentially CellTag cells over the course of a biological process; (iii) profile single-cell transcriptomes via high-throughput droplet-based platforms; and (iv) generate a CellTag expression matrix, followed by clone calling and lineage reconstruction. This lentiviral-labeling approach can be deployed in any organism or in vitro culture system that is amenable to viral transduction to simultaneously profile lineage and identity at single-cell resolution.

Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins

We describe a universal sample multiplexing method for single-cell RNA sequencing in which fixed cells are chemically labeled by attaching identifying DNA oligonucleotides to cellular proteins. Analysis of a 96-plex perturbation experiment revealed changes in cell population structure and transcriptional states that cannot be discerned from bulk measurements, establishing an efficient method for surveying cell populations from large experiments or clinical samples with the depth and resolution of single-cell RNA sequencing.

Single-Cell RNA Sequencing of the Cardiovascular System: New Looks for Old Diseases

Cardiovascular disease encompasses a wide range of conditions, resulting in the highest number of deaths worldwide. The underlying pathologies surrounding cardiovascular disease include a vast and complicated network of both cellular and molecular mechanisms. Unique phenotypic alterations in specific cell types, visualized as varying RNA expression-levels (both coding and non-coding), have been identified as crucial factors in the pathology underlying conditions such as heart failure and atherosclerosis. Recent advances in single-cell RNA sequencing (scRNA-seq) have elucidated a new realm of cell subpopulations and transcriptional variations that are associated with normal and pathological physiology in a wide variety of diseases. This breakthrough in the phenotypical understanding of our cells has brought novel insight into cardiovascular basic science. scRNA-seq allows for separation of widely distinct cell subpopulations which were, until recently, simply averaged together with bulk-tissue RNA-seq. scRNA-seq has been used to identify novel cell types in the heart and vasculature that could be implicated in a variety of disease pathologies. Furthermore, scRNA-seq has been able to identify significant heterogeneity of phenotypes within individual cell subtype populations. The ability to characterize single cells based on transcriptional phenotypes allows researchers the ability to map development of cells and identify changes in specific subpopulations due to diseases at a very high throughput. This review looks at recent scRNA-seq studies of various aspects of the cardiovascular system and discusses their potential value to our understanding of the cardiovascular system and pathology.

MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices

Sample multiplexing facilitates scRNA-seq by reducing costs and identifying artifacts such as cell doublets. However, universal and scalable sample barcoding strategies have not been described. We therefore developed MULTI-seq: multiplexing using lipid-tagged indices for single-cell and single-nucleus RNA sequencing. MULTI-seq reagents can barcode any cell type or nucleus from any species with an accessible plasma membrane. The method involves minimal sample processing, thereby preserving cell viability and endogenous gene expression patterns. When cells are classified into sample groups using MULTI-seq barcode abundances, data quality is improved through doublet identification and recovery of cells with low RNA content that would otherwise be discarded by standard quality-control workflows. We use MULTI-seq to track the dynamics of T-cell activation, perform a 96-plex perturbation experiment with primary human mammary epithelial cells and multiplex cryopreserved tumors and metastatic sites isolated from a patient-derived xenograft mouse model of triple-negative breast cancer.

Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data

Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets. Scrublet avoids the need for expert knowledge or cell clustering by simulating multiplets from the data and building a nearest neighbor classifier. To demonstrate the utility of this approach, we test Scrublet on several datasets that include independent knowledge of cell multiplets. Scrublet is freely available for download at github.com/AllonKleinLab/scrublet.