Microfluidics-free single-cell genomics with templated emulsification

Revealing gene function with statistical inference at single-cell resolution

Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.

Unlocking the Complexity: Exploration of Acute Lymphoblastic Leukemia at the Single Cell Level

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. ALL originates from precursor lymphocytes that acquire multiple genomic changes over time, including chromosomal rearrangements and point mutations. While a large variety of genomic defects was identified and characterized in ALL over the past 30 years, it was only in recent years that the clonal heterogeneity was recognized. Thanks to the latest advancements in single-cell sequencing techniques, which have evolved from the analysis of a few hundred cells to the analysis of thousands of cells simultaneously, the study of tumor heterogeneity now becomes possible. Different modalities can be explored at the single-cell level: DNA, RNA, epigenetic modifications, and intracellular and cell surface proteins. In this review, we describe these techniques and highlight their advantages and limitations in the study of ALL biology. Moreover, multiomics technologies and the incorporation of the spatial dimension can provide insight into intercellular communication. We describe how the different single-cell sequencing technologies help to unravel the molecular complexity of ALL, shedding light on its development, its heterogeneity, its interaction with the leukemia microenvironment and possible relapse mechanisms.

PURE-seq identifies Egr1 as a Potential Master Regulator in Murine Aging by Sequencing Long-Term Hematopoietic Stem Cells

Single-cell transcriptomics is valuable for uncovering individual cell properties, particularly in highly heterogeneous systems. However, this technique often results in the analysis of many well- characterized cells, increasing costs and diluting rare cell populations. To address this, we developed PURE-seq (PIP-seq for Rare-cell Enrichment and Sequencing) for scalable sequencing of rare cells. PURE-seq allows direct cell loading from FACS into PIP-seq reactions, minimizing handling and reducing cell loss. PURE-seq reliably captures rare cells, with 60 minutes of sorting capturing tens of cells at a rarity of 1 in 1,000,000. Using PURE-seq, we investigated murine long- term hematopoietic stem cells and their transcriptomes in the context of hematopoietic aging, identifying Egr1 as a potential master regulator of hematopoiesis in the aging context. PURE-seq offers an accessible and reliable method for isolating and sequencing cells that are currently too rare to capture successfully with existing methods.

Concepts and new developments in droplet-based single cell multi-omics

Single cell sequencing technologies have become a fixture in the molecular profiling of cells due to their ease, flexibility, and commercial availability. In particular, partitioning individual cells inside oil droplets via microfluidic reactions enables transcriptomic or multi-omic measurements for thousands of cells in parallel. Complementing the multitude of biological discoveries from genomics analyses, the past decade has brought new capabilities from assay baselines to enable a deeper understanding of the complex data from single cell multi-omics. Here, we highlight four innovations that have improved the reliability and understanding of droplet microfluidic assays. We emphasize new developments that further orient principles of technology development and guidelines for the design, benchmarking, and implementation of new droplet-based methodologies.

Emerging toolkits for decoding the co-occurrence of modified histones and chromatin proteins

In eukaryotes, DNA is packaged into chromatin with the help of highly conserved histone proteins. Together with DNA-binding proteins, posttranslational modifications (PTMs) on these histones play crucial roles in regulating genome function, cell fate determination, inheritance of acquired traits, cellular states, and diseases. While most studies have focused on individual DNA-binding proteins, chromatin proteins, or histone PTMs in bulk cell populations, such chromatin features co-occur and potentially act cooperatively to accomplish specific functions in a given cell. This review discusses state-of-the-art techniques for the simultaneous profiling of multiple chromatin features in low-input samples and single cells, focusing on histone PTMs, DNA-binding, and chromatin proteins. We cover the origins of the currently available toolkits, compare and contrast their characteristic features, and discuss challenges and perspectives for future applications. Studying the co-occurrence of histone PTMs, DNA-binding proteins, and chromatin proteins in single cells will be central for a better understanding of the biological relevance of combinatorial chromatin features, their impact on genomic output, and cellular heterogeneity.

Novel insights into the heterogeneity of FOXP3 + Treg cells in drug-induced allergic reactions through single-cell transcriptomics

This study uncovers the novel heterogeneity of FOXP3 + regulatory T (Treg) cells and their pivotal role in modulating immune responses during drug-induced allergic reactions, employing cutting-edge single-cell transcriptomics. We established a mouse model for drug-induced allergic reactions and utilized single-cell RNA sequencing (scRNA-seq) to analyze the transcriptomic landscapes of FOXP3 + Treg cells isolated from affected tissues. The study involved both in vitro and in vivo approaches to evaluate the impact of FOXP3 expression levels on the immunoregulatory functions of Treg cells during allergic responses. Techniques included flow cytometry, cluster analysis, principal component analysis (PCA), CCK8 and CSFE assays for cell proliferation, LDH release assays for toxicity, ELISA for cytokine profiling, and CRISPR/Cas9 technology for gene editing. Our findings revealed significant transcriptomic heterogeneity among FOXP3 + Treg cells in the context of drug-induced allergic reactions, with distinct subpopulations exhibiting unique gene expression profiles. This heterogeneity suggests specialized roles in immune regulation. We observed a decrease in the proliferative capacity and cytokine secretion of FOXP3 + Treg cells following allergic stimulation, alongside an increase in reaction toxicity. Manipulating FOXP3 expression levels directly influenced these outcomes, where FOXP3 deletion exacerbated allergic responses, whereas its overexpression mitigated them. Notably, in vivo experiments demonstrated that FOXP3 overexpression significantly reduced the severity of allergic skin reactions in mice. Our study presents novel insights into the heterogeneity and crucial immunoregulatory role of FOXP3 + Treg cells during drug-induced allergic reactions. Overexpression of FOXP3 emerges as a potential therapeutic strategy to alleviate such allergic responses. These findings contribute significantly to our understanding of immune regulation and the development of targeted treatments for drug-induced allergies.

FIND-seq: high-throughput nucleic acid cytometry for rare single-cell transcriptomics

Rare cells have an important role in development and disease, and methods for isolating and studying cell subsets are therefore an essential part of biology research. Such methods traditionally rely on labeled antibodies targeted to cell surface proteins, but large public databases and sophisticated computational approaches increasingly define cell subsets on the basis of genomic, epigenomic and transcriptomic sequencing data. Methods for isolating cells on the basis of nucleic acid sequences powerfully complement these approaches by providing experimental access to cell subsets discovered in cell atlases, as well as those that cannot be otherwise isolated, including cells infected with pathogens, with specific DNA mutations or with unique transcriptional or splicing signatures. We recently developed a nucleic acid cytometry platform called 'focused interrogation of cells by nucleic acid detection and sequencing' (FIND-seq), capable of isolating rare cells on the basis of RNA or DNA markers, followed by bulk or single-cell transcriptomic analysis. This platform has previously been used to characterize the splicing-dependent activation of the transcription factor XBP1 in astrocytes and HIV persistence in memory CD4 T cells from people on long-term antiretroviral therapy. Here, we outline the molecular and microfluidic steps involved in performing FIND-seq, including protocol updates that allow detection and whole transcriptome sequencing of rare HIV-infected cells that harbor genetically intact virus genomes. FIND-seq requires knowledge of microfluidics, optics and molecular biology. We expect that FIND-seq, and this comprehensive protocol, will enable mechanistic studies of rare HIV+ cells, as well as other cell subsets that were previously difficult to recover and sequence.

Development of robust antiviral assays using relevant apical-out human airway organoids

While breakthroughs with organoids have emerged as next-generation in vitro tools, standardization for drug discovery remains a challenge. This work introduces human airway organoids with reversed biopolarity (AORBs), cultured and analyzed in a high-throughput, single-organoid-per-well format, enabling milestones towards standardization. AORBs exhibit a spatio-temporally stable apical-out morphology, facilitating high-yield direct intact-organoid virus infection. Single-cell RNA sequencing and immunohistochemistry confirm the physiologically relevant recapitulation of differentiated human airway epithelia. The cellular tropism of five severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains along with host response differences between Delta, Washington, and Omicron variants, as observed in transcriptomic profiles, also suggest clinical relevance. Dose-response analysis of three well-studied SARS-CoV-2 antiviral compounds (remdesivir, bemnifosbuvir, and nirmatrelvir) demonstrates that AORBs efficiently predict human efficacy, comparable to gold-standard air-liquid interface cultures, but with higher throughput (~10-fold) and fewer cells (~100-fold). This combination of throughput and relevance allows AORBs to robustly detect false negative results in efficacy, preventing irretrievable loss of promising lead compounds. While this work leverages the SARS-CoV-2 study as a proof-of-concept application, the standardization capacity of AORB holds broader implications in line with regulatory efforts to push alternatives to animal studies.

Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling

Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

Integrated Droplet-Based Digital Loop-Mediated Isothermal Amplification Microfluidic Chip with Droplet Generation, Incubation, and Continuous Fluorescence Detection

This study integrated sample partition, incubation, and continuous fluorescence detection on a single microfluidic chip for droplet-based digital Loop-Mediated Isothermal Amplification (LAMP) of nucleic acids. This integration eliminated the need to transfer reactions between different platforms, avoiding sample contamination and loss. Prior to the reaction, filling the channels with an oil phase and adding a glass cover slip on top of the chip overcame the problem of bubble generation in the channels during the LAMP reaction due to heating. Additionally, using two fluorescence intensity thresholds enabled simultaneous detection and counting of positive and negative droplets within a single fluorescence detection channel. The chip can partition approximately 6000 droplets from a 5 µL sample within 10 min, with a droplet diameter of around 110 µm and a coefficient of variation (CV) value of 0.82%. Staphylococcus aureus was quantified via the proposed platform. The results demonstrated a highly accurate correlation coefficient (R = 0.9998), and the detection limit reached a concentration of 1.7 × 102 copies/µL. The entire process of the droplet digital LAMP reaction, from droplet generation to incubation to quantitative results, took a maximum of 70 min.

The heterogeneity of erythroid cells: insight at the single-cell transcriptome level

Erythroid cells, the most prevalent cell type in blood, are one of the earliest products and permeate through the entire process of hematopoietic development in the human body, the oxygen-transporting function of which is crucial for maintaining overall health and life support. Previous investigations into erythrocyte differentiation and development have primarily focused on population-level analyses, lacking the single-cell perspective essential for comprehending the intricate pathways of erythroid maturation, differentiation, and the encompassing cellular heterogeneity. The continuous optimization of single-cell transcriptome sequencing technology, or single-cell RNA sequencing (scRNA-seq), provides a powerful tool for life sciences research, which has a particular superiority in the identification of unprecedented cell subgroups, the analyzing of cellular heterogeneity, and the transcriptomic characteristics of individual cells. Over the past decade, remarkable strides have been taken in the realm of single-cell RNA sequencing technology, profoundly enhancing our understanding of erythroid cells. In this review, we systematically summarize the recent developments in single-cell transcriptome sequencing technology and emphasize their substantial impact on the study of erythroid cells, highlighting their contributions, including the exploration of functional heterogeneity within erythroid populations, the identification of novel erythrocyte subgroups, the tracking of different erythroid lineages, and the unveiling of mechanisms governing erythroid fate decisions. These findings not only invigorate erythroid cell research but also offer new perspectives on the management of diseases related to erythroid cells.

Discovering mechanisms of human genetic variation and controlling cell states at scale

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

Fabrication of Crescent Shaped Microparticles for Particle Templated Droplet Formation

Crescent-shaped hydrogel microparticles are shown to template uniform volume aqueous droplets upon simple mixing with aqueous and oil media for various bioassays. This emerging "lab on a particle" technique requires hydrogel particles with tunable material properties and dimensions. The crescent shape of the particles is attained by aqueous two-phase separation of polymers followed by photopolymerization of the curable precursor. In this work, the phase separation of poly(ethylene glycol) diacrylate (PEGDA, Mw 700) and dextran (Mw 40 000) for tunable manufacturing of crescent-shaped particles is investigated. The particles' morphology is precisely tuned by following a phase diagram, varying the UV intensity, and adjusting the flow rates of various streams. The fabricated particles with variable dimensions encapsulate uniform aqueous droplets upon mixing with an oil phase. The particles are fluorescently labeled with red and blue emitting dyes at variable concentrations to produce six color-coded particles. The blue fluorescent dye shows a moderate response to the pH change. The fluorescently labeled particles are able to tolerate an extremely acidic solution (pH 1) but disintegrate within an extremely basic solution (pH 14). The particle-templated droplets are able to effectively retain the disintegrating particle and the fluorescent signal at pH 14.

Single-cell genomics-based immune and disease monitoring in blood malignancies

Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.

Peripheral endotoxin exposure in mice activates crosstalk between phagocytes in the brain and periphery

Background

Inflammation is a central process of many neurological diseases, and a growing number of studies suggest that non-brain-resident immune cells may contribute to this neuroinflammation. However, the unique contributions of specific immune cell subsets to neuroinflammation are presently unknown, and it is unclear how communication between brain-resident and non-resident immune cells underlies peripheral immune cell involvement in neuroinflammation.

Methods

In this study, we employed the well-established model of lipopolysaccharide (LPS)-induced neuroinflammation and captured brain-resident and non-resident immune cells from the brain and its vasculature by magnetically enriching cell suspensions from the non-perfused brain for CD45 + cells. Then, we identified immune subtype-specific neuroinflammatory processes using single-cell genomics and predicted the crosstalk between immune cell subtypes by analyzing the simultaneous expression of ligands and receptors.

Results

We observed a greater abundance of peripheral phagocytes associated with the brain in this model of neuroinflammation, and report that these professional phagocytes activated similar transcriptional profiles to microglia during LPS-induced neuroinflammation. And, we observed that the probable crosstalk between microglia and peripheral phagocytes was activated in this model while homotypic microglial communication was likely to be decreased.

Conclusions

Our novel findings reveal that microglia signaling to non-brain-resident peripheral phagocytes is preferentially triggered by peripheral inflammation, which is associated with brain infiltration of peripheral cells. Overall, our study supports the involvement of peripheral immune cells in neuroinflammation and suggests several possible molecular signaling pathways between microglia and peripheral cells that may facilitate central-peripheral crosstalk during inflammation. Examining these molecular mediators in human disease and other rodent models may reveal novel targets that modify brain health, especially in comorbidities characterized by peripheral inflammation.

Large-scale neurophysiology and single-cell profiling in human neuroscience

Advances in large-scale single-unit human neurophysiology, single-cell RNA sequencing, spatial transcriptomics and long-term ex vivo tissue culture of surgically resected human brain tissue have provided an unprecedented opportunity to study human neuroscience. In this Perspective, we describe the development of these paradigms, including Neuropixels and recent brain-cell atlas efforts, and discuss how their convergence will further investigations into the cellular underpinnings of network-level activity in the human brain. Specifically, we introduce a workflow in which functionally mapped samples of human brain tissue resected during awake brain surgery can be cultured ex vivo for multi-modal cellular and functional profiling. We then explore how advances in human neuroscience will affect clinical practice, and conclude by discussing societal and ethical implications to consider. Potential findings from the field of human neuroscience will be vast, ranging from insights into human neurodiversity and evolution to providing cell-type-specific access to study and manipulate diseased circuits in pathology. This Perspective aims to provide a unifying framework for the field of human neuroscience as we welcome an exciting era for understanding the functional cytoarchitecture of the human brain.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

Scalable integration of multiomic single-cell data using generative adversarial networks

MOTIVATION

Single-cell profiling has become a common practice to investigate the complexity of tissues, organs, and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome, and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or the very same cells. Yet, integration of more than two assays is currently not supported by the majority of the computational frameworks avaiable.

RESULTS

We here propose a Multi-Omic data integration framework based on Wasserstein Generative Adversarial Networks suitable for the analysis of paired or unpaired data with a high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated.

AVAILABILITY AND IMPLEMENTATION

Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.

Microglia as integrators of brain-associated molecular patterns

Microglia are brain-resident macrophages that play key roles in brain development and experience dependent plasticity. In this review we discuss recent findings regarding the molecular mechanisms through which mammalian microglia sense the unique molecular patterns of the homeostatic brain. We propose that microglial function is acutely controlled in response to 'brain-associated molecular patterns' (BAMPs) that function as indicators of neuronal activity and neural circuit remodeling. A further layer of regulation comes from instructive cytokine cues that define unique microglial functional states. A systematic investigation of the receptors and signaling pathways that mediate these two regulatory axes may begin to define a functional code for microglia-neuron interactions.

Oriented Interpenetrating Capillary Network with Surface Engineering by Porous ZnO from Wood for Membrane Emulsification

Membrane emulsification technology has garnered increasing interest in emulsion preparation due to controllable droplet size, narrower droplet size distribution, low energy consumption, simple process design and excellent reproducibility. Nevertheless, the pore structure and surface engineering in membrane materials design play a crucial role in achieving high-quality emulsions with high throughput simultaneously. In this work, an oriented interpenetrating capillary network composed of highly aligned and interconnected wood cell lumens has been utilized to fabricate an emulsion membrane. A novel honeycomb porous ZnO layer obtained by a seed prefabrication-hydrothermal growth method was designed to reconstruct wood channel surfaces for enhanced microfluid mixing. The results show that through the unique capillary mesh microstructure of wood, the emulsion droplets were smaller in size, had narrower pore-size distribution, and were easy to obtain under high throughput conditions. Meanwhile, a well-designed ZnO layer could further improve the emulsion quality of a wood membrane, while the emulsifying throughput is still maintained at a higher level. This demonstrates that the convection process of the microfluid in these wood capillary channels was intensified markedly. This study not only develops advanced membrane materials in emulsion preparation, but also introduces a brand-new field for functional applications of wood.

Nucleic Acid Plate Culture: Label-Free and Naked-Eye-Based Digital Loop-Mediated Isothermal Amplification in Hydrogel with Machine Learning

Digital nucleic acid amplification enables the absolute quantification of single molecules. However, due to the ultrasmall reaction volume in the digital system (i.e., short light path), most digital systems are limited to fluorescence signals, while label-free and naked-eye readout remain challenging. In this work, we report a digital nucleic acid plate culture method for label-free, ultrasimple, and naked-eye nucleic acid analysis. As simple as the bacteria culture, the nanoconfined digital loop-mediated isothermal amplification was performed by using polyacrylamide (PAM) hydrogel as the amplification matrix. The nanoconfinement of PAM hydrogel with an ionic polymer chain can remarkably accelerate the amplification of target nucleic acids and the growth of inorganic byproducts, namely, magnesium pyrophosphate particles (MPPs). Compared to that in aqueous solutions, MPPs trapped in the hydrogel with enhanced light scattering characteristics are clearly visible to the naked eye, forming white "colony" spots that can be simply counted in a label-free and instrument-free manner. The MPPs can also be photographed by a smartphone and automatically counted by a machine-learning algorithm to realize the absolute quantification of antibiotic-resistant pathogens in diverse real samples.

Immune niches for hair follicle development and homeostasis

The hair follicle is a dynamic mini-organ that has specialized cycles and architectures with diverse cell types to form hairs. Previous studies for several decades have investigated morphogenesis and signaling pathways during embryonic development and adult hair cycles in both mouse and human skin. In particular, hair follicle stem cells and mesenchymal niches received major attention as key players, and their roles and interactions were heavily revealed. Although resident and circulating immune cells affect cellular function and interactions in the skin, research on immune cells has mainly received attention on diseases rather than development or homeostasis. Recently, many studies have suggested the functional roles of diverse immune cells as a niche for hair follicles. Here, we will review recent findings about immune niches for hair follicles and provide insight into mechanisms of hair growth and diseases.

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

BACKGROUND

Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used.

RESULTS

We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required.

CONCLUSION

Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

A Microfluidic Chip for Single-Cell Capture Based on Stagnation Point Flow and Boundary Effects

The capture of individual cells using microfluidic chips represents a widely adopted and efficient approach for investigating the biochemical microenvironment of singular cells. While conventional methods reliant on boundary effects pose challenges in precisely manipulating individual cells, single-cell capture grounded in the principle of stagnation point flow offers a solution to this limitation. Nevertheless, such capture mechanisms encounter inconsistency due to the instability of the flow field and stagnation point. In this study, a microfluidic device for the stable capture of single cells was designed, integrating the principle of fluid mechanics by amalgamating stagnation point flow and boundary effects. This innovative microfluidic chip transcended the limitations associated with single methodologies, leveraging the strengths of both stagnation point flow and boundary effects to achieve reliable single-cell capture. Notably, the incorporation of capture ports at the stagnation point not only harnessed boundary effects but also enhanced capture efficiency significantly, elevating it from 31.9% to 83.3%, thereby augmenting capture stability. Furthermore, computational simulations demonstrated the efficacy of the capture ports in entrapping particles of varying diameters, including 9 μm, 14 μm, and 18 μm. Experiment validation underscored the capability of this microfluidic system to capture single cells within the chip, maintaining stability even under flow rate perturbations spanning from 60 μL/min to 120 μL/min. Consequently, cells with dimensions between 8 μm and 12 μm can be reliably captured. The designed microfluidic system not only furnishes a straightforward and efficient experimental platform but also holds promise for facilitating deeper investigations into the intricate interplay between individual cells and their surrounding microenvironment.

Fast and flexible profiling of chromatin accessibility and total RNA expression in single nuclei using Microwell-seq3

Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.

Advances in the Application of Single-Cell Transcriptomics in Plant Systems and Synthetic Biology

Plants are complex systems hierarchically organized and composed of various cell types. To understand the molecular underpinnings of complex plant systems, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for revealing high resolution of gene expression patterns at the cellular level and investigating the cell-type heterogeneity. Furthermore, scRNA-seq analysis of plant biosystems has great potential for generating new knowledge to inform plant biosystems design and synthetic biology, which aims to modify plants genetically/epigenetically through genome editing, engineering, or re-writing based on rational design for increasing crop yield and quality, promoting the bioeconomy and enhancing environmental sustainability. In particular, data from scRNA-seq studies can be utilized to facilitate the development of high-precision Build-Design-Test-Learn capabilities for maximizing the targeted performance of engineered plant biosystems while minimizing unintended side effects. To date, scRNA-seq has been demonstrated in a limited number of plant species, including model plants (e.g., Arabidopsis thaliana), agricultural crops (e.g., Oryza sativa), and bioenergy crops (e.g., Populus spp.). It is expected that future technical advancements will reduce the cost of scRNA-seq and consequently accelerate the application of this emerging technology in plants. In this review, we summarize current technical advancements in plant scRNA-seq, including sample preparation, sequencing, and data analysis, to provide guidance on how to choose the appropriate scRNA-seq methods for different types of plant samples. We then highlight various applications of scRNA-seq in both plant systems biology and plant synthetic biology research. Finally, we discuss the challenges and opportunities for the application of scRNA-seq in plants.

Preservation of scRNA-Seq Libraries Using Existing Inactivation Protocols

Single-cell RNA sequencing has soared in popularity in recent years. The ability to deeply profile the states of individual cells during the course of disease or infection has helped to expand our knowledge of coordinated responses. However, significant challenges arise when performing this analysis in high containment settings such as biosafety level 3 (BSL-3), BSL-3+ and BSL-4. Working in containment is necessary for many important pathogens, such as Ebola virus, Marburg virus, Lassa virus, Nipah and Hendra viruses. Since standard operating procedures (SOPs) for inactivation are extensive and may compromise sample integrity, we tested whether the removal of single-cell sequencing libraries from containment laboratories using existing inactivation protocols for nucleic acid extraction (Trizol, RLT buffer, or AVL buffer) was feasible. We have demonstrated that the inactivation does not affect sample quality and can work with existing methods for inactivation.

A foundational atlas of autism protein interactions reveals molecular convergence

Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation in Xenopus tropicalis and human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.

A single cell RNAseq benchmark experiment embedding "controlled" cancer heterogeneity

Single-cell RNA sequencing (scRNA-seq) has emerged as a vital tool in tumour research, enabling the exploration of molecular complexities at the individual cell level. It offers new technical possibilities for advancing tumour research with the potential to yield significant breakthroughs. However, deciphering meaningful insights from scRNA-seq data poses challenges, particularly in cell annotation and tumour subpopulation identification. Efficient algorithms are therefore needed to unravel the intricate biological processes of cancer. To address these challenges, benchmarking datasets are essential to validate bioinformatics methodologies for analysing single-cell omics in oncology. Here, we present a 10XGenomics scRNA-seq experiment, providing a controlled heterogeneous environment using lung cancer cell lines characterised by the expression of seven different driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1), leading to partially overlapping functional pathways. Our dataset provides a comprehensive framework for the development and validation of methodologies for analysing cancer heterogeneity by means of scRNA-seq.

Massively parallel single-cell sequencing of diverse microbial populations

Single-cell genetic heterogeneity is ubiquitous in microbial populations and an important aspect of microbial biology; however, we lack a broadly applicable and accessible method to study this heterogeneity in microbial populations. Here, we show a simple, robust and generalizable method for high-throughput single-cell sequencing of target genetic loci in diverse microbes using simple droplet microfluidics devices (droplet targeted amplicon sequencing; DoTA-seq). DoTA-seq serves as a platform to perform diverse assays for single-cell genetic analysis of microbial populations. Using DoTA-seq, we demonstrate the ability to simultaneously track the prevalence and taxonomic associations of >10 antibiotic-resistance genes and plasmids within human and mouse gut microbial communities. This workflow is a powerful and accessible platform for high-throughput single-cell sequencing of diverse microbial populations.

Microfluidics-aided fabrication of 3D micro-nano hierarchical SERS substrate for rapid detection of dual hepatocellular carcinoma biomarkers

The simultaneous analysis of trace amounts of dual biomarkers is crucial in the early diagnosis, treatment, and prognosis of hepatocellular carcinoma (HCC). In this study, we prepared SERS-active hydrogel microparticles (SAHMs) with 3D hierarchical gold nanoparticles (AuNPs) micro-nanostructures by microdroplet technology and in situ synthesis, which demonstrated high reproducibility and sensitivity. Compared with traditional 2D SERS substrates, this newly prepared 3D SERS substrate provided a high density of nano-wrinkled structures and numerous AuNPs. Furthermore, a newly designed SERS-active substrate was proposed for the simultaneous microfluidic detection of AFP and AFU. The Raman signals of sandwich immunocomplexes on the surface of the SAHMs were measured for the trace analysis of these biomarkers. The proposed microfluidic platform achieved AFP and AFU detection in the range of 0.1-100 ng mL-1 and 0.01-100 ng mL-1, respectively, which represents a good response. Indeed, this platform is easy to fabricate, of low cost and has short detection time and comparable detection limits to other methods. As far as we know, this is the first study to achieve the simultaneous detection of AFP and AFU on a microfluidic platform. Therefore, we proposed a new simultaneous detection platform for dual HCC biomarkers that shows strong potential for the early diagnosis of HCC.

Single-Cell RNA Sequencing Technology Landscape in 2023

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and the dynamics of gene expression, bearing profound significance in stem cell research. Depending on the starting materials used for analysis, scRNA-seq encompasses scRNA-seq and single-nucleus RNA sequencing (snRNA-seq). scRNA-seq excels in capturing cellular heterogeneity and characterizing rare cell populations within complex tissues, while snRNA-seq is advantageous in situations where intact cell dissociation is challenging or undesirable (eg, epigenomic studies). A number of scRNA-seq technologies have been developed as of late, including but not limited to droplet-based, plate-based, hydrogel-based, and spatial transcriptomics. The number of cells, sequencing depth, and sequencing length in scRNA-seq can vary across different studies. Addressing current technical challenges will drive the future of scRNA-seq, leading to more comprehensive and precise insights into cellular biology and disease mechanisms informing therapeutic interventions.

Slide-tags enables single-nucleus barcoding for multimodal spatial genomics

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed1-6. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as an input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 μm spatial resolution and delivered whole-transcriptome data that are indistinguishable in quality from ordinary single-nucleus RNA-sequencing data. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualized receptor-ligand interactions driving B cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to almost any single-cell measurement technology. As a proof of principle, we performed multiomic measurements of open chromatin, RNA and T cell receptor (TCR) sequences in the same cells from metastatic melanoma, identifying transcription factor motifs driving cancer cell state transitions in spatially distinct microenvironments. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.

The use of single-cell RNA-seq to study heterogeneity at varying levels of virus-host interactions

The outcome of viral infection depends on the diversity of the infecting viral population and the heterogeneity of the cell population that is infected. Until almost a decade ago, the study of these dynamic processes during viral infection was challenging and limited to certain targeted measurements. Presently, with the use of single-cell sequencing technology, the complex interface defined by the interactions of cells with infecting virus can now be studied across the breadth of the transcriptome in thousands of individual cells simultaneously. In this review, we will describe the use of single-cell RNA sequencing (scRNA-seq) to study the heterogeneity of viral infections, ranging from individual virions to the immune response between infected individuals. In addition, we highlight certain key experimental limitations and methodological decisions that are critical to analyzing scRNA-seq data at each scale.

Marine Invertebrates One Cell at A Time: Insights from Single-Cell Analysis

Over the past decade, single-cell RNA-sequencing (scRNA-seq) has made it possible to study the cellular diversity of a broad range of organisms. Technological advances in single-cell isolation and sequencing have expanded rapidly, allowing the transcriptomic profile of individual cells to be captured. As a result, there has been an explosion of cell type atlases created for many different marine invertebrate species from across the tree of life. Our focus in this review is to synthesize current literature on marine invertebrate scRNA-seq. Specifically, we provide perspectives on key insights from scRNA-seq studies, including descriptive studies of cell type composition, how cells respond in dynamic processes such as development and regeneration, and the evolution of new cell types. Despite these tremendous advances, there also lie several challenges ahead. We discuss the important considerations that are essential when making comparisons between experiments, or between datasets from different species. Finally, we address the future of single-cell analyses in marine invertebrates, including combining scRNA-seq data with other 'omics methods to get a fuller understanding of cellular complexities. The full diversity of cell types across marine invertebrates remains unknown and understanding this diversity and evolution will provide rich areas for future study.

Meta-analysis of single-cell RNA-sequencing data for depicting the transcriptomic landscape of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by airflow limitation and chronic inflammation of the lungs that is a leading cause of death worldwide. Since the complete pathological mechanisms at the single-cell level are not fully understood yet, an integrative approach to characterizing the single-cell-resolution landscape of COPD is required. To identify the cell types and mechanisms associated with the development of COPD, we conducted a meta-analysis using three single-cell RNA-sequencing datasets of COPD. Among the 154,011 cells from 16 COPD patients and 18 healthy subjects, 17 distinct cell types were observed. Of the 17 cell types, monocytes, mast cells, and alveolar type 2 cells (AT2 cells) were found to be etiologically implicated in COPD based on genetic and transcriptomic features. The most transcriptomically diversified states of the three etiological cell types showed significant enrichment in immune/inflammatory responses (monocytes and mast cells) and/or mitochondrial dysfunction (monocytes and AT2 cells). We then identified three chemical candidates that may potentially induce COPD by modulating gene expression patterns in the three etiological cell types. Overall, our study suggests the single-cell level mechanisms underlying the pathogenesis of COPD and may provide information on toxic compounds that could be potential risk factors for COPD.

A roadmap for delivering a human musculoskeletal cell atlas

Advances in single-cell technologies have transformed the ability to identify the individual cell types present within tissues and organs. The musculoskeletal bionetwork, part of the wider Human Cell Atlas project, aims to create a detailed map of the healthy musculoskeletal system at a single-cell resolution throughout tissue development and across the human lifespan, with complementary generation of data from diseased tissues. Given the prevalence of musculoskeletal disorders, this detailed reference dataset will be critical to understanding normal musculoskeletal function in growth, homeostasis and ageing. The endeavour will also help to identify the cellular basis for disease and lay the foundations for novel therapeutic approaches to treating diseases of the joints, soft tissues and bone. Here, we present a Roadmap delineating the critical steps required to construct the first draft of a human musculoskeletal cell atlas. We describe the key challenges involved in mapping the extracellular matrix-rich, but cell-poor, tissues of the musculoskeletal system, outline early milestones that have been achieved and describe the vision and directions for a comprehensive musculoskeletal cell atlas. By embracing cutting-edge technologies, integrating diverse datasets and fostering international collaborations, this endeavour has the potential to drive transformative changes in musculoskeletal medicine.

CRISPR screening in hematology research: from bulk to single-cell level

The CRISPR genome editing technology has revolutionized the way gene function is studied. Genome editing can be achieved in single genes or for thousands of genes simultaneously in sensitive genetic screens. While conventional genetic screens are limited to bulk measurements of cell behavior, recent developments in single-cell technologies make it possible to combine CRISPR screening with single-cell profiling. In this way, cell behavior and gene expression can be monitored simultaneously, with the additional possibility of including data on chromatin accessibility and protein levels. Moreover, the availability of various Cas proteins leading to inactivation, activation, or other effects on gene function further broadens the scope of such screens. The integration of single-cell multi-omics approaches with CRISPR screening open the path to high-content information on the impact of genetic perturbations at single-cell resolution. Current limitations in cell throughput and data density need to be taken into consideration, but new technologies are rapidly evolving and are likely to easily overcome these limitations. In this review, we discuss the use of bulk CRISPR screening in hematology research, as well as the emergence of single-cell CRISPR screening and its added value to the field.

DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods

While single-cell sequencing technologies provide unprecedented insights into genomic profiles at the cellular level, they lose the spatial context of cells. Over the past decade, diverse spatial transcriptomics and multi-omics technologies have been developed to analyze molecular profiles of tissues. In this article, we categorize current spatial genomics technologies into three classes: optical imaging, positional indexing, and mathematical cartography. We discuss trade-offs in resolution and scale, identify limitations, and highlight synergies between existing single-cell and spatial genomics methods. Further, we propose DNA-GPS (global positioning system), a theoretical framework for large-scale optics-free spatial genomics that combines ideas from mathematical cartography and positional indexing. DNA-GPS has the potential to achieve scalable spatial genomics for multiple measurement modalities, and by eliminating the need for optical measurement, it has the potential to position cells in three-dimensions (3D).

Microfluidics-free single-cell genomics reveals complex central-peripheral immune crosstalk in the mouse brain during peripheral inflammation

Inflammation is a realized detriment to brain health in a growing number of neurological diseases, but querying neuroinflammation in its cellular complexity remains a challenge. This manuscript aims to provide a reliable and accessible strategy for examining the brain's immune system. We compare the efficacy of cell isolation methods in producing ample and pure immune samples from mouse brains. Then, with the high-input single-cell genomics platform PIPseq, we generate a rich neuroimmune dataset containing microglia and many peripheral immune populations. To demonstrate this strategy's utility, we interrogate the well-established model of LPS-induced neuroinflammation with single-cell resolution. We demonstrate the activation of crosstalk between microglia and peripheral phagocytes and highlight the unique contributions of microglia and peripheral immune cells to neuroinflammation. Our approach enables the high-depth evaluation of inflammation in longstanding rodent models of neurological disease to reveal novel insight into the contributions of the immune system to brain health.

Modulation of Influenza A virus NS1 expression reveals prioritization of host response antagonism at single-cell resolution

Influenza A virus (IAV) is an important human respiratory pathogen that causes significant seasonal epidemics and potential devastating pandemics. As part of its life cycle, IAV encodes the multifunctional protein NS1, that, among many roles, prevents immune detection and limits interferon (IFN) production. As distinct host immune pathways exert different selective pressures against IAV, as replication progresses, we expect a prioritization in the host immune antagonism by NS1. In this work, we profiled bulk transcriptomic differences in a primary bronchial epithelial cell model facing IAV infections at distinct NS1 levels. We further demonstrated that, at single cell level, the intracellular amount of NS1 in-part shapes the heterogeneity of the host response. We found that modulation of NS1 levels reveal a ranking in its inhibitory roles: modest NS1 expression is sufficient to inhibit immune detection, and thus the expression of pro-inflammatory cytokines (including IFNs), but higher levels are required to inhibit IFN signaling and ISG expression. Lastly, inhibition of chaperones related to the unfolded protein response requires the highest amount of NS1, often associated with later stages of viral replication. This work demystifies some of the multiple functions ascribed to IAV NS1, highlighting the prioritization of NS1 in antagonizing the different pathways involved in the host response to IAV infection.

Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender

Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), generate considerable background noise counts, the hallmark of which is nonzero counts in cell-free droplets and off-target gene expression in unexpected cell types. Such systematic background noise can lead to batch effects and spurious differential gene expression results. Here we develop a deep generative model based on the phenomenology of noise generation in droplet-based assays. The proposed model accurately distinguishes cell-containing droplets from cell-free droplets, learns the background noise profile and provides noise-free quantification in an end-to-end fashion. We implement this approach in the scalable and robust open-source software package CellBender. Analysis of simulated data demonstrates that CellBender operates near the theoretically optimal denoising limit. Extensive evaluations using real datasets and experimental benchmarks highlight enhanced concordance between droplet-based single-cell data and established gene expression patterns, while the learned background noise profile provides evidence of degraded or uncaptured cell types.

Hydrogel-Encapsulated Beads Enable Proximity-Driven Encoded Library Synthesis and Screening

Encoded combinatorial library technologies have dramatically expanded the chemical space for screening but are usually only analyzed by affinity selection binding. It would be highly advantageous to reformat selection outputs to "one-bead-one-compound" solid-phase libraries, unlocking activity-based and cellular screening capabilities. Here, we describe hydrogel-encapsulated magnetic beads that enable such a transformation. Bulk emulsion polymerization of polyacrylamide hydrogel shells around magnetic microbeads yielded uniform particles (7 ± 2 μm diameter) that are compatible with diverse in-gel functionalization (amine, alkyne, oligonucleotides) and transformations associated with DNA-encoded library synthesis (acylation, enzymatic DNA ligation). In a case study of reformatting mRNA display libraries, transcription from DNA-templated magnetic beads encapsulated in gel particles colocalized both RNA synthesis via hybridization with copolymerized complementary DNA and translation via puromycin labeling. Two control epitope templates (V5, HA) were successfully enriched (50- and 99-fold, respectively) from an NNK5 library bead screen via FACS. Proximity-driven library synthesis in concert with magnetic sample manipulation provides a plausible means for reformatting encoded combinatorial libraries at scale.

Single-cell genomics meets human genetics

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.

Understanding virus-host interactions in tissues

Although virus-host interactions are usually studied in a single cell type using in vitro assays in immortalized cell lines or isolated cell populations, it is important to remember that what is happening inside one infected cell does not translate to understanding how an infected cell behaves in a tissue, organ or whole organism. Infections occur in complex tissue environments, which contain a host of factors that can alter the course of the infection, including immune cells, non-immune cells and extracellular-matrix components. These factors affect how the host responds to the virus and form the basis of the protective response. To understand virus infection, tools are needed that can profile the tissue environment. This Review highlights methods to study virus-host interactions in the infection microenvironment.

A contamination focused approach for optimizing the single-cell RNA-seq experiment

Droplet-based single-cell RNA-seq (scRNA-seq) data are plagued by ambient contaminations caused by nucleic acid material released by dead and dying cells. This material is mixed into the buffer and is co-encapsulated with cells, leading to a lower signal-to-noise ratio. Although there exist computational methods to remove ambient contaminations post-hoc, the reliability of algorithms in generating high-quality data from low-quality sources remains uncertain. Here, we assess data quality before data filtering by a set of quantitative, contamination-based metrics that assess data quality more effectively than standard metrics. Through a series of controlled experiments, we report improvements that can minimize ambient contamination outside of tissue dissociation, via cell fixation, improved cell loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms. We provide end-users with insights on factors that can guide their decision-making regarding optimizations that minimize ambient contamination, and metrics to assess data quality.

Single-cell analysis of the nervous system at small and large scales with instant partitions

Single-cell RNA sequencing is a new frontier across all biology, particularly in neuroscience. While powerful for answering numerous neuroscience questions, limitations in sample input size, and initial capital outlay can exclude some researchers from its application. Here, we tested a recently introduced method for scRNAseq across diverse scales and neuroscience experiments. We benchmarked against a major current scRNAseq technology and found that PIPseq performed similarly, in line with earlier benchmarking data. Across dozens of samples, PIPseq recovered many brain cell types at small and large scales (1,000-100,000 cells/sample) and was able to detect differentially expressed genes in an inflammation paradigm. Similarly, PIPseq could detect expected and new differentially expressed genes in a brain single cell suspension from a knockout mouse model; it could also detect rare, virally-la-belled cells following lentiviral targeting and gene knockdown. Finally, we used PIPseq to investigate gene expression in a nontraditional model species, the little skate (Leucoraja erinacea). In total, PIPSeq was able to detect single-cell gene expression changes across models and species, with an added benefit of large scale capture and sequencing of each sample.

Single cell transcriptome analyses of the developing zebrafish eye- perspectives and applications

Within a relatively short period of time, single cell transcriptome analyses (SCT) have become increasingly ubiquitous with transcriptomic research, uncovering plentiful details that boost our molecular understanding of various biological processes. Stemming from SCT analyses, the ever-growing number of newly assigned genetic markers increases our understanding of general function and development, while providing opportunities for identifying genes associated with disease. SCT analyses have been carried out using tissue from numerous organisms. However, despite the great potential of zebrafish as a model organism, other models are still preferably used. In this mini review, we focus on eye research as an example of the advantages in using zebrafish, particularly its usefulness for single cell transcriptome analyses of developmental processes. As studies have already shown, the unique opportunities offered by zebrafish, including similarities to the human eye, in combination with the possibility to analyze and extract specific cells at distinct developmental time points makes the model a uniquely powerful one. Particularly the practicality of collecting large numbers of embryos and therefore isolation of sufficient numbers of developing cells is a distinct advantage compared to other model organisms. Lastly, the advent of highly efficient genetic knockouts methods offers opportunities to characterize target gene function in a more cost-efficient way. In conclusion, we argue that the use of zebrafish for SCT approaches has great potential to further deepen our molecular understanding of not only eye development, but also many other organ systems.