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

Cut&tag: a powerful epigenetic tool for chromatin profiling

Analysis of transcription factors and chromatin modifications at the genome-wide level provides insights into gene regulatory processes, such as transcription, cell differentiation and cellular response. Chromatin immunoprecipitation is the most popular and powerful approach for mapping chromatin, and other enzyme-tethering techniques have recently become available for living cells. Among these, Cleavage Under Targets and Tagmentation (CUT&Tag) is a relatively novel chromatin profiling method that has rapidly gained popularity in the field of epigenetics since 2019. It has also been widely adapted to map chromatin modifications and TFs in different species, illustrating the association of these chromatin epitopes with various physiological and pathological processes. Scalable single-cell CUT&Tag can be combined with distinct platforms to distinguish cellular identity, epigenetic features and even spatial chromatin profiling. In addition, CUT&Tag has been developed as a strategy for joint profiling of the epigenome, transcriptome or proteome on the same sample. In this review, we will mainly consolidate the applications of CUT&Tag and its derivatives on different platforms, give a detailed explanation of the pros and cons of this technique as well as the potential development trends and applications in the future.

A Cysteinyl-tRNA Synthetase Mutation Causes Novel Autosomal-Dominant Inheritance of a Parkinsonism/Spinocerebellar-Ataxia Complex

This study aimed to identify possible pathogenic genes in a 90-member family with a rare combination of multiple neurodegenerative disease phenotypes, which has not been depicted by the known neurodegenerative disease. We performed physical and neurological examinations with International Rating Scales to assess signs of ataxia, Parkinsonism, and cognitive function, as well as brain magnetic resonance imaging scans with seven sequences. We searched for co-segregations of abnormal repeat-expansion loci, pathogenic variants in known spinocerebellar ataxia-related genes, and novel rare mutations via whole-genome sequencing and linkage analysis. A rare co-segregating missense mutation in the CARS gene was validated by Sanger sequencing and the aminoacylation activity of mutant CARS was measured by spectrophotometric assay. This pedigree presented novel late-onset core characteristics including cerebellar ataxia, Parkinsonism, and pyramidal signs in all nine affected members. Brain magnetic resonance imaging showed cerebellar/pons atrophy, pontine-midline linear hyperintensity, decreased rCBF in the bilateral basal ganglia and cerebellar dentate nucleus, and hypo-intensities of the cerebellar dentate nuclei, basal ganglia, mesencephalic red nuclei, and substantia nigra, all of which suggested neurodegeneration. Whole-genome sequencing identified a novel pathogenic heterozygous mutation (E795V) in the CARS gene, meanwhile, exhibited none of the known repeat-expansions or point mutations in pathogenic genes. Remarkably, this CARS mutation causes a 20% decrease in aminoacylation activity to charge tRNACys with L-cysteine in protein synthesis compared with that of the wild type. All family members carrying a heterozygous mutation CARS (E795V) had the same clinical manifestations and neuropathological changes of Parkinsonism and spinocerebellar-ataxia. These findings identify novel pathogenesis of Parkinsonism-spinocerebellar ataxia and provide insights into its genetic architecture.

Sample collection protocol for single-cell RNA sequencing of functionally identified neuronal populations in vivo

Here, we present a sample collection protocol for single-cell RNA sequencing of functionally identified neuronal populations in vivo with a virally delivered activity-dependent labeling tool (CaMPARI2). We describe steps for photoconversion in mice during the onset of computationally relevant events in a virtual reality environment, followed by removal and dissociation of the photo-labeled tissue, and separation of differentially labeled groups with fluorescence-activated cell sorting (FACS). We then detail procedures for characterizing and examining functionally relevant groups using standard bioinformatic techniques. For complete details on the use and execution of this protocol, please refer to O'Toole et al.1.

Single-nucleus chromatin landscape dataset of mouse brain development and aging

The development and aging of the brain constitute a lifelong dynamic process, marked by structural and functional changes that entail highly coordinated cellular differentiation and epigenetic regulatory mechanisms. Chromatin accessibility serves as the foundational basis for genetic activity. However, the holistic and dynamic chromatin landscape that spans various brain regions throughout development and ageing remains predominantly unexplored. In this study, we employed single-nucleus ATAC-seq to generate comprehensive chromatin accessibility maps, incorporating data from 69,178 cells obtained from four distinct brain regions - namely, the olfactory bulb (OB), cerebellum (CB), prefrontal cortex (PFC), and hippocampus (HP) - across key developmental time points at 7 P, 3 M, 12 M, and 18 M. We delineated the distribution of cell types across different age stages and brain regions, providing insight into chromatin accessible regions and key transcription factors specific to different cell types. Our data contribute to understanding the epigenetic basis of the formation of different brain regions, providing a dynamic landscape and comprehensive resource for revealing gene regulatory programs during brain development and aging.

From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology

RNA sequencing (RNAseq) methodology has experienced a burst of technological developments in the last decade, which has opened up opportunities for studying the mechanisms of adaptation to environmental factors at both the organismal and cellular level. Selecting the most suitable experimental approach for specific research questions and model systems can, however, be a challenge and researchers in ecology and evolution are commonly faced with the choice of whether to study gene expression variation in whole bodies, specific tissues, and/or single cells. A wide range of sometimes polarised opinions exists over which approach is best. Here, we highlight the advantages and disadvantages of each of these approaches to provide a guide to help researchers make informed decisions and maximise the power of their study. Using illustrative examples of various ecological and evolutionary research questions, we guide the readers through the different RNAseq approaches and help them identify the most suitable design for their own projects.

Linearly Amplified Single-Stranded RNA-Derived Transcriptome Sequencing (LAST-seq)

Single-cell RNA sequencing (scRNA-seq) stands as a cutting-edge technology widely used in biological and biomedical research. Existing scRNA-seq methods rely on reverse transcription (RT) and second-strand synthesis (SSS) to convert RNA to cDNA before amplification. However, these methods often suffer from limited RT/SSS efficiency, which compromises the sensitivity of RNA detection. Here, we develop a new method, linearly amplified single-stranded RNA-derived transcriptome sequencing (LAST-seq), which directly amplifies the original single-stranded RNA without prior RT and SSS and offers high-sensitivity RNA detection and a low level of technical noise in single-cell transcriptome analysis. LAST-seq has been applied to quantify transcriptional bursting kinetics in human cells, advancing our understanding of chromatin organization's role in regulating gene expression. Key features • An RNase H/DNA polymerase-based strategy to attach the T7 promoter to single-stranded RNA. • T7 promoter mediated IVT on single stranded RNA template at single cell level.

Advances in single-cell long-read sequencing technologies

With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.

Single-cell transcriptomics dissecting the development and evolution of nervous system in insects

Insects can display a vast repertoire of complex and adaptive behaviors crucial for survival and reproduction. Yet, how the neural circuits underlying insect behaviors are assembled throughout development and remodeled during evolution remains largely obscure. The advent of single-cell transcriptomics has opened new paths to illuminate these historically intractable questions. Insect behavior is governed by its brain, whose functional complexity is realized through operations across multiple levels, from the molecular and cellular to the circuit and organ. Single-cell transcriptomics enables dissecting brain functions across all these levels and allows tracking regulatory dynamics throughout development and under perturbation. In this review, we mainly focus on the achievements of single-cell transcriptomics in dissecting the molecular and cellular architectures of nervous systems in representative insects, then discuss its applications in tracking the developmental trajectory and functional evolution of insect brains.

Building and analyzing metacells in single-cell genomics data

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

Effect of genomic and cellular environments on gene expression noise

BACKGROUND

Individual cells from isogenic populations often display large cell-to-cell differences in gene expression. This "noise" in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome.

RESULTS

To address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we perform the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We find that the mean and noise of expression correlate with different histone modifications. We quantify the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assign the extrinsic component to differences between the CD24+ "stem-like" substate and the more "differentiated" substate. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for "safe-harbor" loci.

CONCLUSIONS

Taken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome and that the data generatd by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.

ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets

Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes. Here we provide details for designing large oligonucleotide probe sets for an organism of choice, amplifying probe sets to produce sufficient quantities for repeated experiments, adding unique molecular indexes and poly-A tails to produce finalized probes, in situ probe hybridization and single-cell encapsulation and library preparation. This protocol, from the probe amplification to the library preparation, requires ~7 d to complete. ProBac-seq offers several advantages over other methods by capturing only the desired target sequences and avoiding nondesired transcripts, such as highly abundant ribosomal RNA, thus enriching for signal that better informs on cellular state. The use of multiple probes per gene can detect meaningful single-cell signals from cells expressing transcripts to a lesser degree or those grown in minimal media and other environmentally relevant conditions in which cells are less active. ProBac-seq is also compatible with other organisms that can be profiled by in situ hybridization techniques.

Bidirectional epigenetic editing reveals hierarchies in gene regulation

CRISPR perturbation methods are limited in their ability to study non-coding elements and genetic interactions. In this study, we developed a system for bidirectional epigenetic editing, called CRISPRai, in which we apply activating (CRISPRa) and repressive (CRISPRi) perturbations to two loci simultaneously in the same cell. We developed CRISPRai Perturb-seq by coupling dual perturbation gRNA detection with single-cell RNA sequencing, enabling study of pooled perturbations in a mixed single-cell population. We applied this platform to study the genetic interaction between two hematopoietic lineage transcription factors, SPI1 and GATA1, and discovered novel characteristics of their co-regulation on downstream target genes, including differences in SPI1 and GATA1 occupancy at genes that are regulated through different modes. We also studied the regulatory landscape of IL2 (interleukin-2) in Jurkat T cells, primary T cells and chimeric antigen receptor (CAR) T cells and elucidated mechanisms of enhancer-mediated IL2 gene regulation. CRISPRai facilitates investigation of context-specific genetic interactions, provides new insights into gene regulation and will enable exploration of non-coding disease-associated variants.

Unveiling heterogeneity in MSCs: exploring marker-based strategies for defining MSC subpopulations

Mesenchymal stem/stromal cells (MSCs) represent a heterogeneous cell population distributed throughout various tissues, demonstrating remarkable adaptability to microenvironmental cues and holding immense promise for disease treatment. However, the inherent diversity within MSCs often leads to variability in therapeutic outcomes, posing challenges for clinical applications. To address this heterogeneity, purification of MSC subpopulations through marker-based isolation has emerged as a promising approach to ensure consistent therapeutic efficacy. In this review, we discussed the reported markers of MSCs, encompassing those developed through candidate marker strategies and high-throughput approaches, with the aim of explore viable strategies for addressing the heterogeneity of MSCs and illuminate prospective research directions in this field.

GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells

The organization of mammalian genomes features a complex, multiscale three-dimensional (3D) architecture, whose functional significance remains elusive because of limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we introduce genome architecture and gene expression by sequencing (GAGE-seq), a scalable, robust single-cell co-assay measuring 3D genome structure and transcriptome simultaneously within the same cell. Applied to mouse brain cortex and human bone marrow CD34+ cells, GAGE-seq characterized the intricate relationships between 3D genome and gene expression, showing that multiscale 3D genome features inform cell-type-specific gene expression and link regulatory elements to target genes. Integration with spatial transcriptomic data revealed in situ 3D genome variations in mouse cortex. Observations in human hematopoiesis unveiled discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level. GAGE-seq provides a powerful, cost-effective approach for exploring genome structure and gene expression relationships at the single-cell level across diverse biological contexts.

SCIPAC: quantitative estimation of cell-phenotype associations

Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.

An atlas of spider development at single-cell resolution provides new insights into arthropod embryogenesis

Spiders are a diverse order of chelicerates that diverged from other arthropods over 500 million years ago. Research on spider embryogenesis, particularly studies using the common house spider Parasteatoda tepidariorum, has made important contributions to understanding the evolution of animal development, including axis formation, segmentation, and patterning. However, we lack knowledge about the cells that build spider embryos, their gene expression profiles and fate. Single-cell transcriptomic analyses have been revolutionary in describing these complex landscapes of cellular genetics in a range of animals. Therefore, we carried out single-cell RNA sequencing of P. tepidariorum embryos at stages 7, 8 and 9, which encompass the establishment and patterning of the body plan, and initial differentiation of many tissues and organs. We identified 20 cell clusters, from 18.5 k cells, which were marked by many developmental toolkit genes, as well as a plethora of genes not previously investigated. We found differences in the cell cycle transcriptional signatures, suggestive of different proliferation dynamics, which related to distinctions between endodermal and some mesodermal clusters, compared with ectodermal clusters. We identified many Hox genes as markers of cell clusters, and Hox gene ohnologs were often present in different clusters. This provided additional evidence of sub- and/or neo-functionalisation of these important developmental genes after the whole genome duplication in an arachnopulmonate ancestor (spiders, scorpions, and related orders). We also examined the spatial expression of marker genes for each cluster to generate a comprehensive cell atlas of these embryonic stages. This revealed new insights into the cellular basis and genetic regulation of head patterning, hematopoiesis, limb development, gut development, and posterior segmentation. This atlas will serve as a platform for future analysis of spider cell specification and fate, and studying the evolution of these processes among animals at cellular resolution.

Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression

There are an estimated 300,000 mammalian viruses from which infectious diseases in humans may arise. They inhabit human tissues such as the lungs, blood, and brain and often remain undetected. Efficient and accurate detection of viral infection is vital to understanding its impact on human health and to make accurate predictions to limit adverse effects, such as future epidemics. The increasing use of high-throughput sequencing methods in research, agriculture, and healthcare provides an opportunity for the cost-effective surveillance of viral diversity and investigation of virus-disease correlation. However, existing methods for identifying viruses in sequencing data rely on and are limited to reference genomes or cannot retain single-cell resolution through cell barcode tracking. We introduce a method that accurately and rapidly detects viral sequences in bulk and single-cell transcriptomics data based on highly conserved amino acid domains, which enables the detection of RNA viruses covering up to 1012 virus species. The analysis of viral presence and host gene expression in parallel at single-cell resolution allows for the characterization of host viromes and the identification of viral tropism and host responses. We applied our method to identify putative novel viruses in rhesus macaque PBMC data that display cell type specificity and whose presence correlates with altered host gene expression.

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.

Excitatory Spinal Lhx9-Derived Interneurons Modulate Locomotor Frequency in Mice

Locomotion allows us to move and interact with our surroundings. Spinal networks that control locomotion produce rhythm and left-right and flexor-extensor coordination. Several glutamatergic populations, Shox2 non-V2a, Hb9-derived interneurons, and, recently, spinocerebellar neurons have been proposed to be involved in the mouse rhythm generating networks. These cells make up only a smaller fraction of the excitatory cells in the ventral spinal cord. Here, we set out to identify additional populations of excitatory spinal neurons that may be involved in rhythm generation or other functions in the locomotor network. We use RNA sequencing from glutamatergic, non-glutamatergic, and Shox2 cells in the neonatal mice from both sexes followed by differential gene expression analyses. These analyses identified transcription factors that are highly expressed by glutamatergic spinal neurons and differentially expressed between Shox2 neurons and glutamatergic neurons. From this latter category, we identified the Lhx9-derived neurons as having a restricted spinal expression pattern with no Shox2 neuron overlap. They are purely glutamatergic and ipsilaterally projecting. Ablation of the glutamatergic transmission or acute inactivation of the neuronal activity of Lhx9-derived neurons leads to a decrease in the frequency of locomotor-like activity without change in coordination pattern. Optogenetic activation of Lhx9-derived neurons promotes locomotor-like activity and modulates the frequency of the locomotor activity. Calcium activities of Lhx9-derived neurons show strong left-right out-of-phase rhythmicity during locomotor-like activity. Our study identifies a distinct population of spinal excitatory neurons that regulates the frequency of locomotor output with a suggested role in rhythm-generation in the mouse alongside other spinal populations.

Single-cell analysis reveals context-dependent, cell-level selection of mtDNA

Heteroplasmy occurs when wild-type and mutant mitochondrial DNA (mtDNA) molecules co-exist in single cells1. Heteroplasmy levels change dynamically in development, disease and ageing2,3, but it is unclear whether these shifts are caused by selection or drift, and whether they occur at the level of cells or intracellularly. Here we investigate heteroplasmy dynamics in dividing cells by combining precise mtDNA base editing (DdCBE)4 with a new method, SCI-LITE (single-cell combinatorial indexing leveraged to interrogate targeted expression), which tracks single-cell heteroplasmy with ultra-high throughput. We engineered cells to have synonymous or nonsynonymous complex I mtDNA mutations and found that cell populations in standard culture conditions purge nonsynonymous mtDNA variants, whereas synonymous variants are maintained. This suggests that selection dominates over simple drift in shaping population heteroplasmy. We simultaneously tracked single-cell mtDNA heteroplasmy and ancestry, and found that, although the population heteroplasmy shifts, the heteroplasmy of individual cell lineages remains stable, arguing that selection acts at the level of cell fitness in dividing cells. Using these insights, we show that we can force cells to accumulate high levels of truncating complex I mtDNA heteroplasmy by placing them in environments where loss of biochemical complex I activity has been reported to benefit cell fitness. We conclude that in dividing cells, a given nonsynonymous mtDNA heteroplasmy can be harmful, neutral or even beneficial to cell fitness, but that the 'sign' of the effect is wholly dependent on the environment.

Genome organization around nuclear speckles drives mRNA splicing efficiency

The nucleus is highly organized, such that factors involved in the transcription and processing of distinct classes of RNA are confined within specific nuclear bodies1,2. One example is the nuclear speckle, which is defined by high concentrations of protein and noncoding RNA regulators of pre-mRNA splicing3. What functional role, if any, speckles might play in the process of mRNA splicing is unclear4,5. Here we show that genes localized near nuclear speckles display higher spliceosome concentrations, increased spliceosome binding to their pre-mRNAs and higher co-transcriptional splicing levels than genes that are located farther from nuclear speckles. Gene organization around nuclear speckles is dynamic between cell types, and changes in speckle proximity lead to differences in splicing efficiency. Finally, directed recruitment of a pre-mRNA to nuclear speckles is sufficient to increase mRNA splicing levels. Together, our results integrate the long-standing observations of nuclear speckles with the biochemistry of mRNA splicing and demonstrate a crucial role for dynamic three-dimensional spatial organization of genomic DNA in driving spliceosome concentrations and controlling the efficiency of mRNA splicing.

NKX2-2 based nuclei sorting on frozen human archival pancreas enables the enrichment of islet endocrine populations for single-nucleus RNA sequencing

BACKGROUND

Current approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissolution might alter gene expressions. In this work, we report the development of a single-nucleus RNA sequencing (snRNA-seq) approach with targeted islet cell enrichment for endocrine-population focused transcriptomic profiling using frozen archival pancreatic tissues without islet isolation.

RESULTS

We cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from frozen archival human pancreata. We innovated fluorescence-activated nuclei sorting based on the positive signal of NKX2-2 antibody to enrich nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. In comparison with single-cell RNA sequencing (scRNA-seq) library generated with live cells from freshly isolated human islets, the snRNA-seq library displayed comparable endocrine cellular composition and cell type signature gene expression. However, between these two types of libraries, differential enrichments of transcripts belonging to different functional classes could be observed.

CONCLUSIONS

Our work fills a technological gap and helps to unleash frozen archival pancreatic tissues for molecular profiling targeting the endocrine population. This study opens doors to retrospective mappings of endocrine cell dynamics in pancreatic tissues of complex histopathology. We expect that our protocol is applicable to enrich nuclei for transcriptomics studies from various populations in different types of frozen archival tissues.

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

Corticospinal neurons (CSNs) synapse directly on spinal neurons, a diverse assortment of cells 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, the full diversity of spinal neurons targeted by CFA and RFA CSNs is uncharted. 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 neuron types, with notable exceptions that suggest that these populations differ in how they influence behavior. Finally, axon collaterals of CFA and RFA CSNs target similar brain regions yet receive highly divergent inputs. These results detail the rules of CSN connectivity throughout the brain and spinal cord for two regions critical for forelimb behavior.

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.

Functional diversities within neurons and astrocytes in the adult rat auditory cortex revealed by single-nucleus RNA sequencing

The mammalian cerebral cortex is composed of a rich diversity of cell types. Cortical cells are organized into networks that rely on their functional diversity to ultimately carry out a variety of sophisticated cognitive functions. To investigate the breadth of transcriptional diverse cell types in the sensory cortex, we have used single-nucleus RNA sequencing (snRNA-seq) in the auditory cortex of the adult rat. A variety of unique excitatory and inhibitory neuron types were identified. In addition, we report for the first time a diversity of astrocytes in the auditory cortex that may represent functionally unique subtypes. Together, these results pave the way for building models of how neurons in the sensory cortex work in concert with astrocytes at synapses to fulfill high-cognitive functions like learning and memory.

Annelid adult cell type diversity and their pluripotent cellular origins

Many annelids can regenerate missing body parts or reproduce asexually, generating all cell types in adult stages. However, the putative adult stem cell populations involved in these processes, and the diversity of cell types generated by them, are still unknown. To address this, we recover 75,218 single cell transcriptomes of the highly regenerative and asexually-reproducing annelid Pristina leidyi. Our results uncover a rich cell type diversity including annelid specific types as well as novel types. Moreover, we characterise transcription factors and gene networks that are expressed specifically in these populations. Finally, we uncover a broadly abundant cluster of putative stem cells with a pluripotent signature. This population expresses well-known stem cell markers such as vasa, piwi and nanos homologues, but also shows heterogeneous expression of differentiated cell markers and their transcription factors. We find conserved expression of pluripotency regulators, including multiple chromatin remodelling and epigenetic factors, in piwi+ cells. Finally, lineage reconstruction analyses reveal computational differentiation trajectories from piwi+ cells to diverse adult types. Our data reveal the cell type diversity of adult annelids by single cell transcriptomics and suggest that a piwi+ cell population with a pluripotent stem cell signature is associated with adult cell type differentiation.

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.

Highly Multiplexing, Throughput and Efficient Single-Cell Protein Analysis with Digital Microfluidics

Proteins as crucial components of cells are responsible for the majority of cellular processes. Sensitive and efficient protein detection enables a more accurate and comprehensive investigation of cellular phenotypes and life activities. Here, a protein sequencing method with high multiplexing, high throughput, high cell utilization, and integration based on digital microfluidics (DMF-Protein-seq) is proposed, which transforms protein information into DNA sequencing readout via DNA-tagged antibodies and labels single cells with unique cell barcodes. In a 184-electrode DMF-Protein-seq system, ≈1800 cells are simultaneously detected per experimental run. The digital microfluidics device harnessing low-adsorbed hydrophobic surface and contaminants-isolated reaction space supports high cell utilization (>90%) and high mapping reads (>90%) with the input cells ranging from 140 to 2000. This system leverages split&pool strategy on the DMF chip for the first time to overcome DMF platform restriction in cell analysis throughput and replace the traditionally tedious bench-top combinatorial barcoding. With the benefits of high efficiency and sensitivity in protein analysis, the system offers great potential for cell classification and drug monitoring based on protein expression at the single-cell level.

Potential Roles of Specific Subclasses of Premotor Interneurons in Spinal Cord Function Recovery after Traumatic Spinal Cord Injury in Adults

The differential expression of transcription factors during embryonic development has been selected as the main feature to define the specific subclasses of spinal interneurons. However, recent studies based on single-cell RNA sequencing and transcriptomic experiments suggest that this approach might not be appropriate in the adult spinal cord, where interneurons show overlapping expression profiles, especially in the ventral region. This constitutes a major challenge for the identification and direct targeting of specific populations that could be involved in locomotor recovery after a traumatic spinal cord injury in adults. Current experimental therapies, including electrical stimulation, training, pharmacological treatments, or cell implantation, that have resulted in improvements in locomotor behavior rely on the modulation of the activity and connectivity of interneurons located in the surroundings of the lesion core for the formation of detour circuits. However, very few publications clarify the specific identity of these cells. In this work, we review the studies where premotor interneurons were able to create new intraspinal circuits after different kinds of traumatic spinal cord injury, highlighting the difficulties encountered by researchers, to classify these populations.

Lifelong persistence of nuclear RNAs in the mouse brain

Genomic DNA that resides in the nuclei of mammalian neurons can be as old as the organism itself. The life span of nuclear RNAs, which are critical for proper chromatin architecture and transcription regulation, has not been determined in adult tissues. In this work, we identified and characterized nuclear RNAs that do not turn over for at least 2 years in a subset of postnatally born cells in the mouse brain. These long-lived RNAs were stably retained in nuclei in a neural cell type-specific manner and were required for the maintenance of heterochromatin. Thus, the life span of neural cells may depend on both the molecular longevity of DNA for the storage of genetic information and also the extreme stability of RNA for the functional organization of chromatin.

The rise of single-cell transcriptomics in yeast

The field of single-cell omics has transformed our understanding of biological processes and is constantly advancing both experimentally and computationally. One of the most significant developments is the ability to measure the transcriptome of individual cells by single-cell RNA-seq (scRNA-seq), which was pioneered in higher eukaryotes. While yeast has served as a powerful model organism in which to test and develop transcriptomic technologies, the implementation of scRNA-seq has been significantly delayed in this organism, mainly because of technical constraints associated with its intrinsic characteristics, namely the presence of a cell wall, a small cell size and little amounts of RNA. In this review, we examine the current technologies for scRNA-seq in yeast and highlight their strengths and weaknesses. Additionally, we explore opportunities for developing novel technologies and the potential outcomes of implementing single-cell transcriptomics and extension to other modalities. Undoubtedly, scRNA-seq will be invaluable for both basic and applied yeast research, providing unique insights into fundamental biological processes.

Defining an Optimized Workflow for Enriching and Analyzing Residual Tumor Populations Using Intracellular Markers

Tumor relapse is well recognized to arise from treatment-resistant residual populations. Strategies enriching such populations for in-depth downstream analyses focus on tumor-specific surface markers; however, enrichment using intracellular biomarkers remains challenging. Using B-cell lymphoma as an exemplar, we demonstrate feasibility to enrich B-cell lymphoma 2 (BCL2)high populations, a surrogate marker for t(14;18)+ lymphomas, for use in downstream applications. Different fixation protocols were assessed for impact on antibody expression and RNA integrity; glyoxal fixation demonstrated superior results regarding minimal effects on surface and intracellular expression, and RNA quality, compared with alternative fixatives evaluated. Furthermore, t(14;18)+ B cells were effectively detected using intracellular BCL2 overexpression to facilitate tumor cell enrichment. Tumor cell populations were enriched using the cellenONE F1.4 single-cell sorting platform, which detected and dispensed BCL2high-expressing cells directly into library preparation reagents for transcriptome analyses. Sorted glyoxal-fixed cells generated good quality sequencing libraries, with high concordance between live and fixed single-cell transcriptomic profiles, discriminating cell populations predominantly on B-cell biology. Overall, we successfully developed a proof-of-concept workflow employing a robust cell preparation protocol for intracellular markers combined with cell enrichment using the cellenONE platform, providing an alternative to droplet-based technologies when cellular input is low or requires prior enrichment to detect rare populations. This workflow has wider prognostic and therapeutic potential to study residual cells in a pan-cancer setting.

Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review)

Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.

Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

BIN1K358R suppresses glial response to plaques in mouse model of Alzheimer's disease

INTRODUCTION

The BIN1 coding variant rs138047593 (K358R) is linked to Late-Onset Alzheimer's Disease (LOAD) via targeted exome sequencing.

METHODS

To elucidate the functional consequences of this rare coding variant on brain amyloidosis and neuroinflammation, we generated BIN1K358R knock-in mice using CRISPR/Cas9 technology. These mice were subsequently bred with 5xFAD transgenic mice, which serve as a model for Alzheimer's pathology.

RESULTS

The presence of the BIN1K358R variant leads to increased cerebral amyloid deposition, with a dampened response of astrocytes and oligodendrocytes, but not microglia, at both the cellular and transcriptional levels. This correlates with decreased neurofilament light chain in both plasma and brain tissue. Synaptic densities are significantly increased in both wild-type and 5xFAD backgrounds homozygous for the BIN1K358R variant.

DISCUSSION

The BIN1 K358R variant modulates amyloid pathology in 5xFAD mice, attenuates the astrocytic and oligodendrocytic responses to amyloid plaques, decreases damage markers, and elevates synaptic densities.

HIGHLIGHTS

BIN1 rs138047593 (K358R) coding variant is associated with increased risk of LOAD. BIN1 K358R variant increases amyloid plaque load in 12-month-old 5xFAD mice. BIN1 K358R variant dampens astrocytic and oligodendrocytic response to plaques. BIN1 K358R variant decreases neuronal damage in 5xFAD mice. BIN1 K358R upregulates synaptic densities and modulates synaptic transmission.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

Deciphering the cellular heterogeneity of the insect brain with single-cell RNA sequencing

Insects show highly complicated adaptive and sophisticated behaviors, including spatial orientation skills, learning ability, and social interaction. These behaviors are controlled by the insect brain, the central part of the nervous system. The tiny insect brain consists of millions of highly differentiated and interconnected cells forming a complex network. Decades of research has gone into an understanding of which parts of the insect brain possess particular behaviors, but exactly how they modulate these functional consequences needs to be clarified. Detailed description of the brain and behavior is required to decipher the complexity of cell types, as well as their connectivity and function. Single-cell RNA-sequencing (scRNA-seq) has emerged recently as a breakthrough technology to understand the transcriptome at cellular resolution. With scRNA-seq, it is possible to uncover the cellular heterogeneity of brain cells and elucidate their specific functions and state. In this review, we first review the basic structure of insect brains and the links to insect behaviors mainly focusing on learning and memory. Then the scRNA applications on insect brains are introduced by representative studies. Single-cell RNA-seq has allowed researchers to classify cell subpopulations within different insect brain regions, pinpoint single-cell developmental trajectories, and identify gene regulatory networks. These developments empower the advances in neuroscience and shed light on the intricate problems in understanding insect brain functions and behaviors.

A machine-readable specification for genomics assays

MOTIVATION

Understanding the structure of sequenced fragments from genomics libraries is essential for accurate read preprocessing. Currently, different assays and sequencing technologies require custom scripts and programs that do not leverage the common structure of sequence elements present in genomics libraries.

RESULTS

We present seqspec, a machine-readable specification for libraries produced by genomics assays that facilitates standardization of preprocessing and enables tracking and comparison of genomics assays.

AVAILABILITY AND IMPLEMENTATION

The specification and associated seqspec command line tool is available at https://www.doi.org/10.5281/zenodo.10213865.

Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10x Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10x data where samples were not multiplexed. Despite relatively lower cell capture affecting library preparation, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, a comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.

Best practices for the execution, analysis, and data storage of plant single-cell/nucleus transcriptomics

Single-cell and single-nucleus RNA-sequencing technologies capture the expression of plant genes at an unprecedented resolution. Therefore, these technologies are gaining traction in plant molecular and developmental biology for elucidating the transcriptional changes across cell types in a specific tissue or organ, upon treatments, in response to biotic and abiotic stresses, or between genotypes. Despite the rapidly accelerating use of these technologies, collective and standardized experimental and analytical procedures to support the acquisition of high-quality data sets are still missing. In this commentary, we discuss common challenges associated with the use of single-cell transcriptomics in plants and propose general guidelines to improve reproducibility, quality, comparability, and interpretation and to make the data readily available to the community in this fast-developing field of research.

A molecular atlas of adult C. elegans motor neurons reveals ancient diversity delineated by conserved transcription factor codes

Motor neurons (MNs) constitute an ancient cell type targeted by multiple adult-onset diseases. It is therefore important to define the molecular makeup of adult MNs in animal models and extract organizing principles. Here, we generate a comprehensive molecular atlas of adult Caenorhabditis elegans MNs and a searchable database. Single-cell RNA sequencing of 13,200 cells reveals that ventral nerve cord MNs cluster into 29 molecularly distinct subclasses. Extending C. elegans Neuronal Gene Expression Map and Network (CeNGEN) findings, all MN subclasses are delineated by distinct expression codes of either neuropeptide or transcription factor gene families. Strikingly, combinatorial codes of homeodomain transcription factor genes succinctly delineate adult MN diversity in both C. elegans and mice. Further, molecularly defined MN subclasses in C. elegans display distinct patterns of connectivity. Hence, our study couples the connectivity map of the C. elegans motor circuit with a molecular atlas of its constituent MNs and uncovers organizing principles and conserved molecular codes of adult MN diversity.

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.

Deep model predictive control of gene expression in thousands of single cells

Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system in Escherichia coli cells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the framework's ability to link expression patterns to dynamic functional outcomes by controlling expression of the tetA antibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput without expert knowledge of the biological system.

Enhanced fear memory after social defeat in mice is dependent on interleukin-1 receptor signaling in glutamatergic neurons

Chronic stress is associated with increased anxiety, cognitive deficits, and post-traumatic stress disorder. Repeated social defeat (RSD) in mice causes long-term stress-sensitization associated with increased microglia activation, monocyte accumulation, and enhanced interleukin (IL)-1 signaling in endothelia and neurons. With stress-sensitization, mice have amplified neuronal, immune, and behavioral responses to acute stress 24 days later. This is clinically relevant as it shares key aspects with post-traumatic stress disorder. The mechanisms underlying stress-sensitization are unclear, but enhanced fear memory may be critical. The purpose of this study was to determine the influence of microglia and IL-1R1 signaling in neurons in the development of sensitization and increased fear memory after RSD. Here, RSD accelerated fear acquisition, delayed fear extinction, and increased cued-based freezing at 0.5 day. The enhancement in contextual fear memory after RSD persisted 24 days later. Next, microglia were depleted with a CSF1R antagonist prior to RSD and several parameters were assessed. Microglia depletion blocked monocyte recruitment to the brain. Nonetheless, neuronal reactivity (pCREB) and IL-1β RNA expression in the hippocampus and enhanced fear memory after RSD were microglial-independent. Because IL-1β RNA was prominent in the hippocampus after RSD even with microglia depletion, IL-1R1 mediated signaling in glutamatergic neurons was assessed using neuronal Vglut2+/IL-1R1-/- mice. RSD-induced neuronal reactivity (pCREB) in the hippocampus and enhancement in fear memory were dependent on neuronal IL-1R1 signaling. Furthermore, single-nuclei RNA sequencing (snRNAseq) showed that RSD influenced transcription in specific hippocampal neurons (DG neurons, CA2/3, CA1 neurons) associated with glutamate signaling, inflammation and synaptic plasticity, which were neuronal IL-1R1-dependent. Furthermore, snRNAseq data provided evidence that RSD increased CREB, BDNF, and calcium signaling in DG neurons in an IL-1R1-dependent manner. Collectively, increased IL-1R1-mediated signaling (monocytes/microglia independent) in glutamatergic neurons after RSD enhanced neuronal reactivity and fear memory.

High-throughput omics technologies in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic, relapsing intestinal disease. Elucidation of the pathogenic mechanisms of IBD requires high-throughput technologies (HTTs) to effectively obtain and analyze large amounts of data. Recently, HTTs have been widely used in IBD, including genomics, transcriptomics, proteomics, microbiomics, metabolomics and single-cell sequencing. When combined with endoscopy, the application of these technologies can provide an in-depth understanding on the alterations of intestinal microbe diversity and abundance, the abnormalities of signaling pathway-mediated immune responses and functionality, and the evaluation of therapeutic effects, improving the accuracy of early diagnosis and treatment of IBD. This review comprehensively summarizes the development and advancement of HTTs, and also highlights the challenges and future directions of these technologies in IBD research. Although HTTs have made striking breakthrough in IBD, more standardized methods and large-scale dataset processing are still needed to achieve the goal of personalized medicine.

Advances in single-cell omics and multiomics for high-resolution molecular profiling

Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

Comparison of high-throughput single-cell RNA-seq methods for ex vivo drug screening

Functional precision medicine (FPM) aims to optimize patient-specific drug selection based on the unique characteristics of their cancer cells. Recent advancements in high throughput ex vivo drug profiling have accelerated interest in FPM. Here, we present a proof-of-concept study for an integrated experimental system that incorporates ex vivo treatment response with a single-cell gene expression output enabling barcoding of several drug conditions in one single-cell sequencing experiment. We demonstrate this through a proof-of-concept investigation focusing on the glucocorticoid-resistant acute lymphoblastic leukemia (ALL) E/R+ Reh cell line. Three different single-cell transcriptome sequencing (scRNA-seq) approaches were evaluated, each exhibiting high cell recovery and accurate tagging of distinct drug conditions. Notably, our comprehensive analysis revealed variations in library complexity, sensitivity (gene detection), and differential gene expression detection across the methods. Despite these differences, we identified a substantial transcriptional response to fludarabine, a highly relevant drug for treating high-risk ALL, which was consistently recapitulated by all three methods. These findings highlight the potential of our integrated approach for studying drug responses at the single-cell level and emphasize the importance of method selection in scRNA-seq studies. Finally, our data encompassing 27 327 cells are freely available to extend to future scRNA-seq methodological comparisons.

Single cell RNA-seq: a novel tool to unravel virus-host interplay

Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.

Single-cell transcriptomics is revolutionizing the improvement of plant biotechnology research: recent advances and future opportunities

Single-cell approaches are a promising way to obtain high-resolution transcriptomics data and have the potential to revolutionize the study of plant growth and development. Recent years have seen the advent of unprecedented technological advances in the field of plant biology to study the transcriptional information of individual cells by single-cell RNA sequencing (scRNA-seq). This review focuses on the modern advancements of single-cell transcriptomics in plants over the past few years. In addition, it also offers a new insight of how these emerging methods will expedite advance research in plant biotechnology in the near future. Lastly, the various technological hurdles and inherent limitations of single-cell technology that need to be conquered to develop such outstanding possible knowledge gain is critically analyzed and discussed.