Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding

Profiling joint tissues at single-cell resolution: advances and insights

Advances in the profiling of human joint tissues at single-cell resolution have provided unique insights into the organization and function of these tissues in health and disease. Data generated by various single-cell technologies, including single-cell RNA sequencing and cytometry by time-of-flight, have identified the distinct subpopulations that constitute these tissues. These timely studies have provided the building blocks for the construction of single-cell atlases of joint tissues including cartilage, bone and synovium, leading to the identification of developmental trajectories, deciphering of crosstalk between cells and discovery of rare populations such as stem and progenitor cells. In addition, these studies have revealed unique pathogenetic populations that are potential therapeutic targets. The use of these approaches in synovial tissues has helped to identify how distinct cell subpopulations can orchestrate disease initiation and progression and be responsible for distinct pathological outcomes. Additionally, repair of tissues such as cartilage and meniscus remains an unmet medical need, and single-cell methodologies can be invaluable in providing a blueprint for both effective tissue-engineering strategies and therapeutic interventions for chronic joint diseases such as osteoarthritis and rheumatoid arthritis.

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.

Integrated single-cell multiomics uncovers foundational regulatory mechanisms of lens development and pathology

Ocular lens development entails epithelial to fiber cell differentiation, defects in which cause congenital cataracts. We report the first single-cell multiomic atlas of lens development, leveraging snRNA-seq, snATAC-seq and CUT&RUN-seq to discover previously unreported mechanisms of cell fate determination and cataract-linked regulatory networks. A comprehensive profile of cis- and trans-regulatory interactions, including for the cataract-linked transcription factor MAF, is established across a temporal trajectory of fiber cell differentiation. Furthermore, we identify an epigenetic paradigm of cellular differentiation, defined by progressive loss of the H3K27 methylation writer Polycomb repressive complex 2 (PRC2). PRC2 localizes to heterochromatin domains across master-regulator transcription factor gene bodies, suggesting it safeguards epithelial cell fate. Moreover, we demonstrate that FGF hyper-stimulation in vivo leads to MAF network activation and the emergence of novel lens cell states. Collectively, these data depict a comprehensive portrait of lens fiber cell differentiation, while defining regulatory effectors of cell identity and cataract formation.

Insect Insights at the Single-Cell Level: Technologies and Applications

Single-cell techniques are a promising way to unravel the complexity and heterogeneity of transcripts at the cellular level and to reveal the composition of different cell types and functions in a tissue or organ. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have further changed our view of biological systems. The application of scRNA-seq in insects enables the comprehensive characterization of both common and rare cell types and cell states, the discovery of new cell types, and revealing how cell types relate to each other. The recent application of scRNA-seq techniques to insect tissues has led to a number of exciting discoveries. Here we provide an overview of scRNA-seq and its application in insect research, focusing on biological applications, current challenges, and future opportunities to make new discoveries with scRNA-seq in insects.

Advancements in CRISPR screens for the development of cancer immunotherapy strategies

CRISPR screen technology enables systematic and scalable interrogation of gene function by using the CRISPR-Cas9 system to perturb gene expression. In the field of cancer immunotherapy, this technology has empowered the discovery of genes, biomarkers, and pathways that regulate tumor development and progression, immune reactivity, and the effectiveness of immunotherapeutic interventions. By conducting large-scale genetic screens, researchers have successfully identified novel targets to impede tumor growth, enhance anti-tumor immune responses, and surmount immunosuppression within the tumor microenvironment (TME). Here, we present an overview of CRISPR screens conducted in tumor cells for the purpose of identifying novel therapeutic targets. We also explore the application of CRISPR screens in immune cells to propel the advancement of cell-based therapies, encompassing T cells, natural killer cells, dendritic cells, and macrophages. Furthermore, we outline the crucial components necessary for the successful implementation of immune-specific CRISPR screens and explore potential directions for future research.

Comparison of Single Cell Transcriptome Sequencing Methods: Of Mice and Men

Single cell RNAseq has been a big leap in many areas of biology. Rather than investigating gene expression on a whole organism level, this technology enables scientists to get a detailed look at rare single cells or within their cell population of interest. The field is growing, and many new methods appear each year. We compared methods utilized in our core facility: Smart-seq3, PlexWell, FLASH-seq, VASA-seq, SORT-seq, 10X, Evercode, and HIVE. We characterized the equipment requirements for each method. We evaluated the performances of these methods based on detected features, transcriptome diversity, mitochondrial RNA abundance and multiplets, among others and benchmarked them against bulk RNA sequencing. Here, we show that bulk transcriptome detects more unique transcripts than any single cell method. While most methods are comparable in many regards, FLASH-seq and VASA-seq yielded the best metrics, e.g., in number of features. If no equipment for automation is available or many cells are desired, then HIVE or 10X yield good results. In general, more recently developed methods perform better. This also leads to the conclusion that older methods should be phased out, and that the development of single cell RNAseq methods is still progressing considerably.

Characterization of human pluripotent stem cell differentiation by single-cell dual-omics analyses

In vivo differentiation of human pluripotent stem cells (hPSCs) has unique advantages, such as multilineage differentiation, angiogenesis, and close cell-cell interactions. To systematically investigate multilineage differentiation mechanisms of hPSCs, we constructed the in vivo hPSC differentiation landscape containing 239,670 cells using teratoma models. We identified 43 cell types, inferred 18 cell differentiation trajectories, and characterized common and specific gene regulation patterns during hPSC differentiation at both transcriptional and epigenetic levels. Additionally, we developed the developmental single-cell Basic Local Alignment Search Tool (dscBLAST), an R-based cell identification tool, to simplify the identification processes of developmental cells. Using dscBLAST, we aligned cells in multiple differentiation models to normally developing cells to further understand their differentiation states. Overall, our study offers new insights into stem cell differentiation and human embryonic development; dscBLAST shows favorable cell identification performance, providing a powerful identification tool for developmental cells.

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.

Flexible parsing, interpretation, and editing of technical sequences with splitcode

Next-generation sequencing libraries are constructed with numerous synthetic constructs such as sequencing adapters, barcodes, and unique molecular identifiers. Such sequences can be essential for interpreting results of sequencing assays, and when they contain information pertinent to an experiment, they must be processed and analyzed. We present a tool called splitcode, that enables flexible and efficient parsing, interpreting, and editing of sequencing reads. This versatile tool facilitates simple, reproducible preprocessing of reads from libraries constructed for a large array of single-cell and bulk sequencing assays.

Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses

Patient-derived organoids (PDOs) can model personalized therapy responses; however, current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly multiplexed mass cytometry platform to measure post-translational modification (PTM) signaling, DNA damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer-associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed "Trellis"-a highly scalable, tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rarer, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate PDO plasticity-shifting proliferative colonic stem cells (proCSCs) to slow-cycling revival colonic stem cells (revCSCs) to protect cancer cells from chemotherapy.

Spinal-Specific Super Enhancer in Neuropathic Pain

Dysfunctional gene expression in nociceptive pathways plays a critical role in the development and maintenance of neuropathic pain. Super enhancers (SEs), composed of a large cluster of transcriptional enhancers, are emerging as new players in the regulation of gene expression. However, whether SEs participate in nociceptive responses remains unknown. Here, we report a spinal-specific SE (SS-SE) that regulates chronic constriction injury (CCI)-induced neuropathic pain by driving Ntmt1 and Prrx2 transcription in dorsal horn neurons. Peripheral nerve injury significantly enhanced the activity of SS-SE and increased the expression of NTMT1 and PRRX2 in the dorsal horn of male mice in a bromodomain-containing protein 4 (BRD4)-dependent manner. Both intrathecal administration of a pharmacological BRD4 inhibitor JQ1 and CRISPR-Cas9-mediated SE deletion abolished the increased NTMT1 and PRRX2 in CCI mice and attenuated their nociceptive hypersensitivities. Furthermore, knocking down Ntmt1 or Prrx2 with siRNA suppressed the injury-induced elevation of phosphorylated extracellular-signal-regulated kinase (p-ERK) and glial fibrillary acidic protein (GFAP) expression in the dorsal horn and alleviated neuropathic pain behaviors. Mimicking the increase in spinal Ntmt1 or Prrx2 in naive mice increased p-ERK and GFAP expression and led to the genesis of neuropathic pain-like behavior. These results redefine our understanding of the regulation of pain-related genes and demonstrate that BRD4-driven increases in SS-SE activity is responsible for the genesis of neuropathic pain through the governance of NTMT1 and PRRX2 expression in dorsal horn neurons. Our findings highlight the therapeutic potential of BRD4 inhibitors for the treatment of neuropathic pain.SIGNIFICANCE STATEMENT SEs drive gene expression by recruiting master transcription factors, cofactors, and RNA polymerase, but their role in the development of neuropathic pain remains unknown. Here, we report that the activity of an SS-SE, located upstream of the genes Ntmt1 and Prrx2, was elevated in the dorsal horn of mice with neuropathic pain. SS-SE contributes to the genesis of neuropathic pain by driving expression of Ntmt1 and Prrx2 Both inhibition of SS-SE with a pharmacological BRD4 inhibitor and genetic deletion of SS-SE attenuated pain hypersensitivities. This study suggests an effective and novel therapeutic strategy for neuropathic pain.

A high-throughput single-cell RNA expression profiling method identifies human pericyte markers

AIMS

We sought to identify and optimise a universally available histological marker for pericytes in the human brain. Such a marker could be a useful tool for researchers. Further, identifying a gene expressed relatively specifically in human pericytes could provide new insights into the biological functions of this fascinating cell type.

METHODS

We analysed single-cell RNA expression profiles derived from different human and mouse brain regions using a high-throughput and low-cost single-cell transcriptome sequencing method called EasySci. Through this analysis, we were able to identify specific gene markers for pericytes, some of which had not been previously characterised. We then used commercially (and therefore universally) available antibodies to immunolabel the pericyte-specific gene products in formalin-fixed paraffin-embedded (FFPE) human brains and also performed immunoblots to determine whether appropriately sized proteins were recognised.

RESULTS

In the EasySci data sets, highly pericyte-enriched expression was notable for SLC6A12 and SLC19A1. Antibodies against these proteins recognised bands of approximately the correct size in immunoblots of human brain extracts. Following optimisation of the immunohistochemical technique, staining for both antibodies was strongly positive in small blood vessels and was far more effective than a PDGFRB antibody at staining pericyte-like cells in FFPE human brain sections. In an exploratory sample of other human organs (kidney, lung, liver, muscle), immunohistochemistry did not show the same pericyte-like pattern of staining.

CONCLUSIONS

The SLC6A12 antibody was well suited for labelling pericytes in human FFPE brain sections, based on the combined results of single-cell RNA-seq analyses, immunoblots and immunohistochemical studies.

A global view of aging and Alzheimer's pathogenesis-associated cell population dynamics and molecular signatures in human and mouse brains

Conventional methods fall short in unraveling the dynamics of rare cell types related to aging and diseases. Here we introduce EasySci, an advanced single-cell combinatorial indexing strategy for exploring age-dependent cellular dynamics in the mammalian brain. Profiling approximately 1.5 million single-cell transcriptomes and 400,000 chromatin accessibility profiles across diverse mouse brains, we identified over 300 cell subtypes, uncovering their molecular characteristics and spatial locations. This comprehensive view elucidates rare cell types expanded or depleted upon aging. We also investigated cell-type-specific responses to genetic alterations linked to Alzheimer's disease, identifying associated rare cell types. Additionally, by profiling 118,240 human brain single-cell transcriptomes, we discerned cell- and region-specific transcriptomic changes tied to Alzheimer's pathogenesis. In conclusion, this research offers a valuable resource for probing cell-type-specific dynamics in both normal and pathological aging.

Automatic quality control of single-cell and single-nucleus RNA-seq using valiDrops

Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) measures gene expression in individual cells or nuclei enabling comprehensive characterization of cell types and states. However, isolation of cells or nuclei for sxRNA-seq releases contaminating RNA, which can distort biological signals, through, for example, cell damage and transcript leakage. Thus, identifying barcodes containing high-quality cells or nuclei is a critical analytical step in the processing of sxRNA-seq data. Here, we present valiDrops, an automated method to identify high-quality barcodes and flag dead cells. In valiDrops, barcodes are initially filtered using data-adaptive thresholding on community-standard quality metrics, and subsequently, valiDrops uses a novel clustering-based approach to identify barcodes with distinct biological signals. We benchmark valiDrops and show that biological signals from cell types and states are more distinct, easier to separate and more consistent after filtering by valiDrops compared to existing tools. Finally, we show that valiDrops can predict and flag dead cells with high accuracy. This novel classifier can further improve data quality or be used to identify dead cells to interrogate the biology of cell death. Thus, valiDrops is an effective and easy-to-use method to improve data quality and biological interpretation. Our method is openly available as an R package at www.github.com/madsen-lab/valiDrops.

Pooled Genome-Scale CRISPR Screens in Single Cells

Assigning functions to genes and learning how to control their expression are part of the foundation of cell biology and therapeutic development. An efficient and unbiased method to accomplish this is genetic screening, which historically required laborious clone generation and phenotyping and is still limited by scale today. The rapid technological progress on modulating gene function with CRISPR-Cas and measuring it in individual cells has now relaxed the major experimental constraints and enabled pooled screening with complex readouts from single cells. Here, we review the principles and practical considerations for pooled single-cell CRISPR screening. We discuss perturbation strategies, experimental model systems, matching the perturbation to the individual cells, reading out cell phenotypes, and data analysis. Our focus is on single-cell RNA sequencing and cell sorting-based readouts, including image-enabled cell sorting. We expect this transformative approach to fuel biomedical research for the next several decades.

Topographical and cell type-specific connectivity of rostral and caudal forelimb corticospinal neuron populations

Corticospinal neurons (CSNs) synapse directly on spinal neurons, a diverse group of neurons with unique structural and functional properties necessary for body movements. CSNs modulating forelimb behavior fractionate into caudal forelimb area (CFA) and rostral forelimb area (RFA) motor cortical populations. Despite their prominence, no studies have mapped the diversity of spinal cell types targeted by CSNs, let alone compare CFA and RFA populations. Here we use anatomical and RNA-sequencing methods to show that CSNs synapse onto a remarkably selective group of spinal cell types, favoring inhibitory populations that regulate motoneuron activity and gate sensory feedback. CFA and RFA CSNs target similar spinal cell types, with notable exceptions that suggest these populations differ in how they influence behavior. Finally, axon collaterals of CFA and RFA CSNs target similar brain regions yet receive surprisingly divergent inputs. These results detail the rules of CSN connectivity throughout the brain and spinal cord for two regions critical for forelimb behavior.

Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer

Advances in chimeric antigen receptor (CAR)-T cell therapy have significantly improved clinical outcomes of patients with relapsed or refractory hematologic malignancies. However, progress is still hindered as clinical benefit is only available for a fraction of patients. A lack of understanding of CAR-T cell behaviors in vivo at the single-cell level impedes their more extensive application in clinical practice. Mounting evidence suggests that single-cell sequencing techniques can help perfect the receptor design, guide gene-based T cell modification, and optimize the CAR-T manufacturing conditions, and all of them are essential for long-term immunosurveillance and more favorable clinical outcomes. The information generated by employing these methods also potentially informs our understanding of the numerous complex factors that dictate therapeutic efficacy and toxicities. In this review, we discuss the reasons why CAR-T immunotherapy fails in clinical practice and what this field has learned since the milestone of single-cell sequencing technologies. We further outline recent advances in the application of single-cell analyses in CAR-T immunotherapy. Specifically, we provide an overview of single-cell studies focusing on target antigens, CAR-transgene integration, and preclinical research and clinical applications, and then discuss how it will affect the future of CAR-T cell therapy.

Human Plaque Myofibroblasts to Study Mechanisms of Atherosclerosis

Background Plaque myofibroblasts are critical players in the initiation and advancement of atherosclerotic disease. They are involved in the production of extracellular matrix, the formation of the fibrous cap, and the underlying lipidic core via modulation processes in response to different environmental cues. Despite clear phenotypic differences between myofibroblast cells and healthy vascular smooth muscle cells, smooth muscle cells are still widely used as a cellular model in atherosclerotic research. Methods and Results Here, we present a conditioned outgrowth method to isolate and culture myofibroblast cells from plaques. We obtained these cells from 27 donors (24 carotid and 3 femoral endarterectomies). We show that they keep their proliferative capacity for 8 passages, are transcriptionally stable, retain donor-specific gene expression programs, and express extracellular matrix proteins (FN1, COL1A1, and DCN) and smooth muscle cell markers (ACTA2, MYH11, and CNN1). Single-cell transcriptomics reveals that the cells in culture closely resemble the plaque myofibroblasts. Chromatin immunoprecipitation sequencing shows the presence of histone H3 lysine 4 dimethylation at the MYH11 promoter, pointing to their smooth muscle cell origin. Finally, we demonstrated that plaque myofibroblasts can be efficiently transduced (>97%) and are capable of taking up oxidized low-density lipoprotein and undergoing calcification. Conclusions In conclusion, we present a method to isolate and culture cells that retain plaque myofibroblast phenotypical and functional capabilities, making them a suitable in vitro model for studying selected mechanisms of atherosclerosis.

[Omics to serve myology]

Despite efforts in biomedical research, pathophysiological mechanisms and therapeutic targets of diseases remain difficult to identify. The development of high-throughput techniques led to the advent of innovatve technologies called omics. They aim at characterizing as exhaustively as possible a set of molecules: genes, RNAs, proteins, metabolites, etc. These a priori methods allow a precise molecular characterization of diseases and a better understanding of complex pathophysiological mechanisms. In this paper, we will review most omics approaches, their integration and their applications in the context of myology.

Microfluidics-free single-cell genomics with templated emulsification

Current single-cell RNA-sequencing approaches have limitations that stem from the microfluidic devices or fluid handling steps required for sample processing. We develop a method that does not require specialized microfluidic devices, expertise or hardware. Our approach is based on particle-templated emulsification, which allows single-cell encapsulation and barcoding of cDNA in uniform droplet emulsions with only a vortexer. Particle-templated instant partition sequencing (PIP-seq) accommodates a wide range of emulsification formats, including microwell plates and large-volume conical tubes, enabling thousands of samples or millions of cells to be processed in minutes. We demonstrate that PIP-seq produces high-purity transcriptomes in mouse-human mixing studies, is compatible with multiomics measurements and can accurately characterize cell types in human breast tissue compared to a commercial microfluidic platform. Single-cell transcriptional profiling of mixed phenotype acute leukemia using PIP-seq reveals the emergence of heterogeneity within chemotherapy-resistant cell subsets that were hidden by standard immunophenotyping. PIP-seq is a simple, flexible and scalable next-generation workflow that extends single-cell sequencing to new applications.

Single-Nucleus RNA-Sequencing in Brain Tissue

Neuroscience research greatly benefits from single-cell sequencing technologies, which can reveal transcriptional alterations on a cellular level. However, preparing single-cell suspensions is technically challenging, requires experience, and has several limitations that can influence the transcriptional readout. Performing sequencing of single nuclei instead of single cells alleviates several of the challenges of sample preparation and highlights acute nuclear transcription. Here, we provide a protocol to prepare a nuclei suspension for single-nucleus RNA-sequencing for cell type-specific transcriptional profiling of brain tissue using the 10x Genomics single-cell gene expression assay. Furthermore, we highlight important aspects to consider during experimental design and data analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Preparation of single-nucleus suspension Basic Protocol 2: Preparation and sequencing of single-nucleus libraries for RNA-seq.

MicroRNA-218 instructs proper assembly of hippocampal networks

The assembly of the mammalian brain is orchestrated by temporally coordinated waves of gene expression. Post-transcriptional regulation by microRNAs (miRNAs) is a key aspect of this program. Indeed, deletion of neuron-enriched miRNAs induces strong developmental phenotypes, and miRNA levels are altered in patients with neurodevelopmental disorders. However, the mechanisms used by miRNAs to instruct brain development remain largely unexplored. Here, we identified miR-218 as a critical regulator of hippocampal assembly. MiR-218 is highly expressed in the hippocampus and enriched in both excitatory principal neurons (PNs) and GABAergic inhibitory interneurons (INs). Early life inhibition of miR-218 results in an adult brain with a predisposition to seizures. Changes in gene expression in the absence of miR-218 suggest that network assembly is impaired. Indeed, we find that miR-218 inhibition results in the disruption of early depolarizing GABAergic signaling, structural defects in dendritic spines, and altered intrinsic membrane excitability. Conditional knockout of Mir218-2 in INs, but not PNs, is sufficient to recapitulate long-term instability. Finally, de-repressing Kif21b and Syt13, two miR-218 targets, phenocopies the effects on early synchronous network activity induced by miR-218 inhibition. Taken together, the data suggest that miR-218 orchestrates formative events in PNs and INs to produce stable networks.

Bibliometric analysis of research on neurodegenerative diseases and single-cell RNA sequencing: Opportunities and challenges

Neurodegeneration, characterized by the progressive deterioration in neuronal structure or function, presents an elusive mechanism. The use of single-cell RNA sequencing (scRNA-seq) technology in the clinic is becoming increasingly prevalent in recent decades. This technology offers unparalleled cell-level insights into neurodegenerative diseases, establishing itself as a potent tool for elucidating these diseases underlying mechanisms. Here, we made a deep investigation for scRNA-seq research in neurodegenerative diseases using bibliometric analysis from 2009 to 2022. We observed a robust upward trajectory in the number of publications on this subject. The United States stood out as the principal contributor to this expanding field. Specifically, the University of California System exhibited notable research prowess in this field. Alzheimer disease and Parkinson disease were the diseases most frequently investigated. Key research hotspots include the creation of a molecular brain atlas and identification of vulnerable neuronal subpopulations and potential therapeutic targets at the transcriptomic level.

Optimized miR-124 reporters uncover differences in miR-124 expression among neuronal populations in vitro

Introduction

Although intensively studied in the last decades, how microRNAs (miRNAs) are expressed across different cell types in the brain remains largely unknown.

Materials

To address this issue, we sought to develop optimized fluorescence reporters that could be expressed in precise cellular subsets and used to accurately quantify miR contents in vivo.

Results

Focusing on miR-124, we tested different reporter designs whose efficiency was confirmed in different in vitro settings including cell lines and primary neuronal cultures from different brain structures. Unlike previous reporters, we provide experimental evidence that our optimized designs can faithfully translate miR levels in vitro.

Discussion

Tools developed here would enable assessing miRNA expression at the single cell resolution and are expected to significantly contribute to future miRNA research in vivo.

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).

Single-cell analysis and spatial resolution of the gut microbiome

Over the past decade it has become clear that various aspects of host physiology, metabolism, and immunity are intimately associated with the microbiome and its interactions with the host. Specifically, the gut microbiome composition and function has been shown to play a critical role in the etiology of different intestinal and extra-intestinal diseases. While attempts to identify a common pattern of microbial dysbiosis linked with these diseases have failed, multiple studies show that bacterial communities in the gut are spatially organized and that disrupted spatial organization of the gut microbiome is often a common underlying feature of disease pathogenesis. As a result, focus over the last few years has shifted from analyzing the diversity of gut microbiome by sequencing of the entire microbial community, towards understanding the gut microbiome in spatial context. Defining the composition and spatial heterogeneity of the microbiome is critical to facilitate further understanding of the gut microbiome ecology. Development in single cell genomics approach has advanced our understanding of microbial community structure, however, limitations in approaches exist. Single cell genomics is a very powerful and rapidly growing field, primarily used to identify the genetic composition of microbes. A major challenge is to isolate single cells for genomic analyses. This review summarizes the different approaches to study microbial genomes at single-cell resolution. We will review new techniques for microbial single cell sequencing and summarize how these techniques can be applied broadly to answer many questions related to the microbiome composition and spatial heterogeneity. These methods can be used to fill the gaps in our understanding of microbial communities.

simpleaf: a simple, flexible, and scalable framework for single-cell data processing using alevin-fry

SUMMARY

The alevin-fry ecosystem provides a robust and growing suite of programs for single-cell data processing. However, as new single-cell technologies are introduced, as the community continues to adjust best practices for data processing, and as the alevin-fry ecosystem itself expands and grows, it is becoming increasingly important to manage the complexity of alevin-fry's single-cell preprocessing workflows while retaining the performance and flexibility that make these tools enticing. We introduce simpleaf, a program that simplifies the processing of single-cell data using tools from the alevin-fry ecosystem, and adds new functionality and capabilities, while retaining the flexibility and performance of the underlying tools.

AVAILABILITY AND IMPLEMENTATION

Simpleaf is written in Rust and released under a BSD 3-Clause license. It is freely available from its GitHub repository https://github.com/COMBINE-lab/simpleaf, and via bioconda. Documentation for simpleaf is available at https://simpleaf.readthedocs.io/en/latest/ and tutorials for simpleaf that have been developed can be accessed at https://combine-lab.github.io/alevin-fry-tutorials.

The organization of spinal neurons: Insights from single cell sequencing

To understand how the spinal cord enacts complex sensorimotor functions, researchers have studied, classified, and functionally probed it's many neuronal populations for over a century. Recent developments in single-cell RNA-sequencing can characterize the gene expression signatures of the entire set of spinal neuron types and can simultaneously provide an unbiased view of their relationships to each other. This approach has revealed that the location of neurons predicts transcriptomic variability, as dorsal spinal neurons become highly distinct over development as ventral spinal neurons become less so. Temporal specification is also a major source of gene expression variation, subdividing many of the canonical embryonic lineage domains. Together, birthdate and cell body location are fundamental organizing features of spinal neuron diversity.

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism

Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-cell ATAC-seq data and embedding attention mechanisms needs to be improved. To this end, we present IscPAM, an interpretable method based on deep learning with an attention mechanism to predict single-cell transcription factors. Our model adopts the convolution neural network to extract the data feature and optimize the pre-trained model. In particular, the model obtains faster training and prediction due to the embedded attention mechanism. For datasets, we take ATAC-seq, ChIP-seq, and DNA sequences data for the pre-trained model, and single-cell ATAC-seq data is used to predict the TF binding graph in the given cell. We verify the interpretability of the model through ablation experiments and sensitivity analysis. IscPAM can efficiently predict the combination of whole genome transcription factors in single cells and study cellular heterogeneity through chromatin accessibility of related diseases.

Transcriptome Profiling of miRNA-mRNA Interactions and Associated Mechanisms in Chemotherapy-Induced Neuropathic Pain

Chemotherapy-induced neuropathic pain (CINP) is a dose-limiting adverse event affecting 40% of chemotherapy patients. MiRNA-mRNA interaction plays an important role in various processes. However, detailed profiling of miRNA-mRNA interactions in CINP remains unclear. Here, a rat-based CINP model was established using paclitaxel, followed by nociceptive behavioral tests related to mechanical allodynia, thermal hyperalgesia, and cold allodynia. The landscape of miRNA-mRNA interaction in the spinal dorsal horn was investigated through mRNA transcriptomics and small RNA sequencing. Under CINP condition, 86 differentially expressed mRNAs and 56 miRNAs were identified. Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated the activity of Odorant binding, postsynaptic specialization and synaptic density, extracellular matrix, mitochondrial matrix, retrograde endocannabinoid signaling, and GTPase activity. Protein-protein interaction (PPI), networks of circRNA-miRNA-mRNA, lncRNA-miRNA-mRNA, and TF-genes were demonstrated. We next explored the immune infiltration microenvironment and found a higher infiltration abundance of Th17 and a lower abundance of MDSC in CINP. RT-qPCR and dual-luciferase assays were used to verify the sequencing results, and single-cell analysis based on the SekSeeq database was conducted. Combined with bioinformatics analyses and experimental validations, Mpz, a protein-coding gene specifically expressed in Schwann cells, was found critical in maintaining CINP under miRNA regulation. Therefore, these data highlight the expression patterns of miRNA-mRNA, and the underlying mechanism in the spinal dorsal horn under CINP condition, and Mpz may serve as a promising therapeutic target for patients with CINP.

Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection

Bacterial populations are highly adaptive. They can respond to stress and survive in shifting environments. How the behaviours of individual bacteria vary during stress, however, is poorly understood. To identify and characterize rare bacterial subpopulations, technologies for single-cell transcriptional profiling have been developed. Existing approaches show some degree of limitation, for example, in terms of number of cells or transcripts that can be profiled. Due in part to these limitations, few conditions have been studied with these tools. Here we develop massively-parallel, multiplexed, microbial sequencing (M3-seq)-a single-cell RNA-sequencing platform for bacteria that pairs combinatorial cell indexing with post hoc rRNA depletion. We show that M3-seq can profile bacterial cells from different species under a range of conditions in single experiments. We then apply M3-seq to hundreds of thousands of cells, revealing rare populations and insights into bet-hedging associated with stress responses and characterizing phage infection.

Sleep Disorder Kleine-Levin Syndrome (KLS) Joins the List of Polygenic Brain Disorders Associated with Obstetric Complications

Kleine-Levin Syndrome is a rare neurological disorder with onset typically during adolescence that is characterized by recurrent episodes of hypersomnia, behavioral changes, and cognitive abnormalities, in the absence of structural changes in neuroimaging. As for many functional brain disorders, the exact disease mechanism in Kleine-Levin Syndrome is presently unknown, preventing the development of specific treatment approaches or protective measures. Here we review the pathophysiology and genetics of this functional brain disorder and then present a specific working hypothesis. A neurodevelopmental mechanism has been suspected based on associations with obstetric complications. Recent studies have focused on genetic factors whereby the first genome-wide association study (GWAS) in Kleine-Levin Syndrome has defined a linkage at the TRANK1 locus. A Gene x Environment interaction model involving obstetric complications was proposed based on concepts developed for other functional brain disorders. To stimulate future research, we here performed annotations of the genes under consideration for Kleine-Levin Syndrome in relation to factors expected to be associated with obstetric complications. Annotations used data-mining of gene/protein lists related to for hypoxia, ischemia, and vascular factors and targeted literature searches. Tentative links for TRANK1, four additional genes in the TRANK1 locus, and LMOD3-LMO2 are described. Protein interaction data for TRANK1 indicate links to CBX2, CBX4, and KDM3A, that in turn can be tied to hypoxia. Taken together, the neurological sleep disorder, Kleine-Levin Syndrome, shows genetic and mechanistic overlap with well analyzed brain disorders such as schizophrenia, autism spectrum disorder and ADHD in which polygenic predisposition interacts with external events during brain development, including obstetric complications.

The technological landscape and applications of single-cell multi-omics

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

HybriSeq: Probe-based Device-free Single-cell RNA Profiling

We have developed the HybriSeq method for single-cell RNA profiling, which utilizes in situ hybridization of multiple probes for targeted transcripts, followed by split-pool barcoding and sequencing analysis of the probes. We have shown that HybriSeq can achieve high sensitivity for RNA detection with multiple probes and profile RNA accessibility. The utility of HybriSeq is demonstrated in characterizing cell-to-cell heterogeneities of a panel of 95 cell-cycle-related genes and the probe-probe heterogeneity within a single transcript.

Tracking cell-type-specific temporal dynamics in human and mouse brains

Progenitor cells are critical in preserving organismal homeostasis, yet their diversity and dynamics in the aged brain remain underexplored. We introduced TrackerSci, a single-cell genomic method that combines newborn cell labeling and combinatorial indexing to characterize the transcriptome and chromatin landscape of proliferating progenitor cells in vivo. Using TrackerSci, we investigated the dynamics of newborn cells in mouse brains across various ages and in a mouse model of Alzheimer's disease. Our dataset revealed diverse progenitor cell types in the brain and their epigenetic signatures. We further quantified aging-associated shifts in cell-type-specific proliferation and differentiation and deciphered the associated molecular programs. Extending our study to the progenitor cells in the aged human brain, we identified conserved genetic signatures across species and pinpointed region-specific cellular dynamics, such as the reduced oligodendrogenesis in the cerebellum. We anticipate that TrackerSci will be broadly applicable to unveil cell-type-specific temporal dynamics in diverse systems.

Organization of Purkinje cell development by neuronal MEGF11 in cerebellar granule cells

As cerebellar granule cells (GCs) coordinate the formation of regular cerebellar networks during postnatal development, molecules in GCs are expected to be involved. Here, we test the effects of the knockdown (KD) of multiple epidermal growth factor-like domains protein 11 (MEGF11), which is a homolog of proteins mediating astrocytic phagocytosis but is substantially increased at the later developmental stages of GCs on cerebellar development. MEGF11-KD in GCs of developing mice results in abnormal cerebellar structures, including extensively ectopic Purkinje cell (PC) somas, and in impaired motor functions. MEGF11-KD also causes abnormally asynchronous synaptic release from GC axons, parallel fibers, before the appearance of abnormal cerebellar structures. Interestingly, blockade of this abnormal synaptic release restores most of the cerebellar structures. Thus, apart from phagocytic functions of its related homologs in astrocytes, MEGF11 in GCs promotes proper PC development and cerebellar network formation by regulating immature synaptic transmission.

Multimodal deep learning approaches for single-cell multi-omics data integration

Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.

Developmental Mouse Brain Common Coordinate Framework

3D standard reference brains serve as key resources to understand the spatial organization of the brain and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of standard 3D reference atlases for developing mouse brains has hindered advancement of our understanding of brain development. Here, we present a multimodal 3D developmental common coordinate framework (DevCCF) spanning mouse embryonic day (E) 11.5, E13.5, E15.5, E18.5, and postnatal day (P) 4, P14, and P56 with anatomical segmentations defined by a developmental ontology. At each age, the DevCCF features undistorted morphologically averaged atlas templates created from Magnetic Resonance Imaging and co-registered high-resolution templates from light sheet fluorescence microscopy. Expert-curated 3D anatomical segmentations at each age adhere to an updated prosomeric model and can be explored via an interactive 3D web-visualizer. As a use case, we employed the DevCCF to unveil the emergence of GABAergic neurons in embryonic brains. Moreover, we integrated the Allen CCFv3 into the P56 template with stereotaxic coordinates and mapped spatial transcriptome cell-type data with the developmental ontology. In summary, the DevCCF is an openly accessible resource that can be used for large-scale data integration to gain a comprehensive understanding of brain development.

Universal preprocessing of single-cell genomics data

We describe a workflow for preprocessing a wide variety of single-cell genomics data types. The approach is based on parsing of machine-readable seqspec assay specifications to customize inputs for kb-python, which uses kallisto and bustools to catalog reads, error correct barcodes, and count reads. The universal preprocessing method is implemented in the Python package cellatlas that is available for download at: https://github.com/cellatlas/cellatlas/.

Single-Nucleus RNA-Seq: Open the Era of Great Navigation for FFPE Tissue

Single-cell sequencing (scRNA-seq) has revolutionized our ability to explore heterogeneity and genetic variations at the single-cell level, opening up new avenues for understanding disease mechanisms and cell-cell interactions. Single-nucleus RNA-sequencing (snRNA-seq) is emerging as a promising solution to scRNA-seq due to its reduced ionized transcription bias and compatibility with richer samples. This approach will provide an exciting opportunity for in-depth exploration of billions of formalin-fixed paraffin-embedded (FFPE) tissues. Recent advancements in single-cell/nucleus gene expression workflows tailored for FFPE tissues have demonstrated their feasibility and provided crucial guidance for future studies utilizing FFPE specimens. In this review, we provide a broad overview of the nuclear preparation strategies, the latest technologies of snRNA-seq applicable to FFPE samples. Finally, the limitations and potential technical developments of snRNA-seq in FFPE samples are summarized. The development of snRNA-seq technologies for FFPE samples will lay a foundation for transcriptomic studies of valuable samples in clinical medicine and human sample banks.

Digital-scRRBS: A Cost-Effective, Highly Sensitive, and Automated Single-Cell Methylome Analysis Platform via Digital Microfluidics

Single-cell DNA methylation sequencing is highly effective for identifying cell subpopulations and constructing epigenetic regulatory networks. Existing methylome analyses require extensive starting materials and are costly, complex, and susceptible to contamination, thereby impeding the development of single-cell methylome technology. In this work, we report digital microfluidics-based single-cell reduced representation bisulfite sequencing (digital-scRRBS), the first microfluidics-based single-cell methylome library construction platform, which is an automatic, effective, reproducible, and reagent-efficient technique to dissect the single-cell methylome. Using our digital microfluidic chip, we isolated single cells in 15 s and successfully constructed single-cell methylation sequencing libraries with a unique genome mapping rate of up to 53.6%, covering up to 2.26 million CpG sites. Digital-scRRBS demonstrates a high capacity for distinguishing cell identity and tracking DNA methylation during drug administration. Digital-scRRBS expands the applicability of single-cell methylation methods as a versatile tool for epigenetic analysis of rare cells and populations with high levels of heterogeneity.

Cerebrospinal fluid-contacting neurons: multimodal cells with diverse roles in the CNS

The cerebrospinal fluid (CSF) is a complex solution that circulates around the CNS, and whose composition changes as a function of an animal's physiological state. Ciliated neurons that are bathed in the CSF - and thus referred to as CSF-contacting neurons (CSF-cNs) - are unusual polymodal interoceptive neurons. As chemoreceptors, CSF-cNs respond to variations in pH and osmolarity and to bacterial metabolites in the CSF. Their activation during infections of the CNS results in secretion of compounds to enhance host survival. As mechanosensory neurons, CSF-cNs operate together with an extracellular proteinaceous polymer known as the Reissner fibre to detect compression during spinal curvature. Once activated, CSF-cNs inhibit motor neurons, premotor excitatory neurons and command neurons to enhance movement speed and stabilize posture. At longer timescales, CSF-cNs instruct morphogenesis throughout life via the release of neuropeptides that act over long distances on skeletal muscle. Finally, recent evidence suggests that mouse CSF-cNs may act as neural stem cells in the spinal cord, inspiring new paths of investigation for repair after injury.

Epigenetic regulation of HBV-specific tumor-infiltrating T cells in HBV-related HCC

BACKGROUND AND AIMS

HBV shapes the T-cell immune responses in HBV-related HCC. T cells can be recruited to the nidus, but limited T cells participate specifically in response to the HBV-related tumor microenvironment and HBV antigens. How epigenomic programs regulate T-cell compartments in virus-specific immune processes is unclear.

APPROACH AND RESULTS

We developed Ti-ATAC-seq. 2 to map the T-cell receptor repertoire, epigenomic, and transcriptomic landscape of αβ T cells at both the bulk-cell and single-cell levels in 54 patients with HCC. We deeply investigated HBV-specific T cells and HBV-related T-cell subsets that specifically responded to HBV antigens and the HBV + tumor microenvironment, respectively, characterizing their T-cell receptor clonality and specificity and performing epigenomic profiling. A shared program comprising NFKB1/2-, Proto-Oncogene, NF-KB Sub unit, NFATC2-, and NR4A1-associated unique T-cell receptor-downstream core epigenomic and transcriptomic regulome commonly regulated the differentiation of HBV-specific regulatory T-cell (Treg) cells and CD8 + exhausted T cells; this program was also selectively enriched in the HBV-related Treg-CTLA4 and CD8-exhausted T cell-thymocyte selection associated high mobility subsets and drove greater clonal expansion in HBV-related Treg-CTLA4 subset. Overall, 54% of the effector and memory HBV-specific T cells are governed by transcription factor motifs of activator protein 1, NFE2, and BACH1/2, which have been reported to be associated with prolonged patient relapse-free survival. Moreover, HBV-related tumor-infiltrating Tregs correlated with both increased viral titer and poor prognosis in patients.

CONCLUSIONS

This study provides insight into the cellular and molecular basis of the epigenomic programs that regulate the differentiation and generation of HBV-related T cells from viral infection and HBV + HCC unique immune exhaustion.

Single cell genomics as a transformative approach for aquaculture research and innovation

Single cell genomics encompasses a suite of rapidly maturing technologies that measure the molecular profiles of individual cells within target samples. These approaches provide a large up-step in biological information compared to long-established 'bulk' methods that profile the average molecular profiles of all cells in a sample, and have led to transformative advances in understanding of cellular biology, particularly in humans and model organisms. The application of single cell genomics is fast expanding to non-model taxa, including aquaculture species, where numerous research applications are underway with many more envisaged. In this review, we highlight the potential transformative applications of single cell genomics in aquaculture research, considering barriers and potential solutions to the broad uptake of these technologies. Focusing on single cell transcriptomics, we outline considerations for experimental design, including the essential requirement to obtain high quality cells/nuclei for sequencing in ectothermic aquatic species. We further outline data analysis and bioinformatics considerations, tailored to studies with the under-characterized genomes of aquaculture species, where our knowledge of cellular heterogeneity and cell marker genes is immature. Overall, this review offers a useful source of knowledge for researchers aiming to apply single cell genomics to address biological challenges faced by the global aquaculture sector though an improved understanding of cell biology.

Neuroplasticity and regeneration after spinal cord injury

Spinal cord injury (SCI) is a debilitating condition with significant personal, societal, and economic burden. The highest proportion of traumatic injuries occur at the cervical level, which results in severe sensorimotor and autonomic deficits. Following the initial physical damage associated with traumatic injuries, secondary pro-inflammatory, excitotoxic, and ischemic cascades are initiated further contributing to neuronal and glial cell death. Additionally, emerging evidence has begun to reveal that spinal interneurons undergo subtype specific neuroplastic circuit rearrangements in the weeks to months following SCI, contributing to or hindering functional recovery. The current therapeutic guidelines and standards of care for SCI patients include early surgery, hemodynamic regulation, and rehabilitation. Additionally, preclinical work and ongoing clinical trials have begun exploring neuroregenerative strategies utilizing endogenous neural stem/progenitor cells, stem cell transplantation, combinatorial approaches, and direct cell reprogramming. This review will focus on emerging cellular and noncellular regenerative therapies with an overview of the current available strategies, the role of interneurons in plasticity, and the exciting research avenues enhancing tissue repair following SCI.

Advances in single-cell RNA sequencing and its applications in cancer research

Cancers are a group of heterogeneous diseases characterized by the acquisition of functional capabilities during the transition from a normal to a neoplastic state. Powerful experimental and computational tools can be applied to elucidate the mechanisms of occurrence, progression, metastasis, and drug resistance; however, challenges remain. Bulk RNA sequencing techniques only reflect the average gene expression in a sample, making it difficult to understand tumor heterogeneity and the tumor microenvironment. The emergence and development of single-cell RNA sequencing (scRNA-seq) technologies have provided opportunities to understand subtle changes in tumor biology by identifying distinct cell subpopulations, dissecting the tumor microenvironment, and characterizing cellular genomic mutations. Recently, scRNA-seq technology has been increasingly used in cancer studies to explore tumor heterogeneity and the tumor microenvironment, which has increased the understanding of tumorigenesis and evolution. This review summarizes the basic processes and development of scRNA-seq technologies and their increasing applications in cancer research and clinical practice.

Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq

Bacteria colonize almost all parts of the human body and can differ significantly. However, the population level transcriptomics measurements can only describe the average bacteria population behaviors, ignoring the heterogeneity among bacteria. Here, we report a droplet-based high-throughput single-microbe RNA-seq assay (smRandom-seq), using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion for mRNA enrichment. smRandom-seq showed a high species specificity (99%), a minor doublet rate (1.6%), a reduced rRNA percentage (32%), and a sensitive gene detection (a median of ~1000 genes per single E. coli). Furthermore, smRandom-seq successfully captured transcriptome changes of thousands of individual E. coli and discovered a few antibiotic resistant subpopulations displaying distinct gene expression patterns of SOS response and metabolic pathways in E. coli population upon antibiotic stress. smRandom-seq provides a high-throughput single-microbe transcriptome profiling tool that will facilitate future discoveries in microbial resistance, persistence, microbe-host interaction, and microbiome research.