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

Rapid emergence of transcriptional heterogeneity upon molecular stress predisposes cells to two distinct states of senescence

Slowing aging can reduce the risk of chronic diseases. In particular, eliminating senescent cells is a promising approach to slow aging. Previous studies found that both cells from older animals and senescent cells have noisy gene expression. Here, we performed a large-scale single-cell RNA-sequencing time course to understand how transcriptional heterogeneity develops among senescent cells. We found that cells experiencing senescence-inducing oxidative stress rapidly adopt one of two major transcriptional states. One senescent cell state is associated with stress response, and the other is associated with tissue remodeling. We did not observe increased stochastic gene expression. This data is consistent with the idea that reproducible, limited, distinct, and coherent transcriptional states exist in senescent cell populations. These physiologically distinct senescent cell subtypes may each affect the aging process in unique ways and constitute a source of heterogeneity in aging.

Combining long-term circuit mapping and network transcriptomics with SiR-N2c

An exciting frontier in circuit neuroscience lies at the intersection between neural network mapping and single-cell genomics. Monosynaptic rabies viruses provide a promising platform for the merger of circuit mapping methods with -omics approaches. However, three key limitations have hindered the extraction of physiologically meaningful gene expression profiles from rabies-mapped circuits: inherent viral cytotoxicity, high viral immunogenicity and virus-induced alteration of cellular transcriptional regulation. These factors alter the transcriptional and translational profiles of infected neurons and their neighboring cells. To overcome these limitations we applied a self-inactivating genomic modification to the less immunogenic rabies strain, CVS-N2c, to generate a self-inactivating CVS-N2c rabies virus (SiR-N2c). SiR-N2c not only eliminates undesired cytotoxic effects but also substantially reduces gene expression alterations in infected neurons and dampens the recruitment of innate and acquired immune responses, thus enabling open-ended interventions on neural networks and their genetic characterization using single-cell genomic approaches.

simpleaf: A simple, flexible, and scalable framework for single-cell transcriptomics 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 are being developed that can be accessed at https://combine-lab.github.io/alevin-fry-tutorials .

RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding

Progress in sample preparation for scRNA-seq is reported based on RevGel-seq, a reversible-hydrogel technology optimized for samples of fresh cells. Complexes of one cell paired with one barcoded bead are stabilized by a chemical linker and dispersed in a hydrogel in the liquid state. Upon gelation on ice the complexes are immobilized and physically separated without requiring nanowells or droplets. Cell lysis is triggered by detergent diffusion, and RNA molecules are captured on the adjacent barcoded beads for further processing with reverse transcription and preparation for cDNA sequencing. As a proof of concept, analysis of PBMC using RevGel-seq achieves results similar to microfluidic-based technologies when using the same original sample and the same data analysis software. In addition, a clinically relevant application of RevGel-seq is presented for pancreatic islet cells. Furthermore, characterizations carried out on cardiomyocytes demonstrate that the hydrogel technology readily accommodates very large cells. Standard analyses are in the 10,000-input cell range with the current gelation device, in order to satisfy common requirements for single-cell research. A convenient stopping point after two hours has been established by freezing at the cell lysis step, with full preservation of gene expression profiles. Overall, our results show that RevGel-seq represents an accessible and efficient instrument-free alternative, enabling flexibility in terms of experimental design and timing of sample processing, while providing broad coverage of cell types.

Unraveling Psychiatric Disorders through Neural Single-Cell Transcriptomics Approaches

The development of single-cell and single-nucleus transcriptome technologies is enabling the unraveling of the molecular and cellular heterogeneity of psychiatric disorders. The complexity of the brain and the relationships between different brain regions can be better understood through the classification of individual cell populations based on their molecular markers and transcriptomic features. Analysis of these unique cell types can explain their involvement in the pathology of psychiatric disorders. Recent studies in both human and animal models have emphasized the importance of transcriptome analysis of neuronal cells in psychiatric disorders but also revealed critical roles for non-neuronal cells, such as oligodendrocytes and microglia. In this review, we update current findings on the brain transcriptome and explore molecular studies addressing transcriptomic alterations identified in human and animal models in depression and stress, neurodegenerative disorders (Parkinson's and Alzheimer's disease), schizophrenia, opioid use disorder, and alcohol and psychostimulant abuse. We also comment on potential future directions in single-cell and single-nucleus studies.

Spatial transcriptomics using multiplexed deterministic barcoding in tissue

Spatially resolved transcriptomics of tissue sections enables advances in fundamental and applied biomedical research. Here, we present Multiplexed Deterministic Barcoding in Tissue (xDBiT) to acquire spatially resolved transcriptomes of nine tissue sections in parallel. New microfluidic chips were developed to spatially encode mRNAs over a total tissue area of 1.17 cm² with a 50 µm resolution. Optimization of the biochemical protocol increased read and gene counts per spot by one order of magnitude compared to previous reports. Furthermore, the introduction of alignment markers allowed seamless registration of images and spatial transcriptomic spots. Together with technological advances, we provide an open-source computational pipeline to prepare raw sequencing data for downstream analysis. The functionality of xDBiT was demonstrated by acquiring 16 spatially resolved transcriptomic datasets from five different murine organs, including the cerebellum, liver, kidney, spleen, and heart. Factor analysis and deconvolution of spatial transcriptomes allowed for in-depth characterization of the murine kidney.

New aspects for the brain in Hartnup disease based on mining of high-resolution cellular mRNA expression data for SLC6A19

Hartnup disease is an autosomal recessive, metabolic disorder caused by mutations of the neutral amino acid transporter, SLC6A19/B0AT1. Reduced absorption in the intestine and kidney results in deficiencies in neutral amino acids and their down-stream metabolites, including niacin, associated with skin lesions and neurological symptoms. The effects on the nervous system such as ataxia have been related to systemic deficiencies of tryptophan (and other neutral amino acids) as no expression of the B0AT1 transporter was found in the brain. In the intestine, SLC6A19 cooperates with ACE2 which has received major attention as the cellular receptor for SARS-CoV-2. When transcriptomics data for ACE2 and its partner proteins were examined, a previously unrecognized expression of Slc6a19 mRNA in the ependymal cells of the mouse brain was encountered that is set into the context of neurological manifestations of Hartnup disease with this communication. A novel role for SLC6A19/B0AT1 in amino acid transport from CSF into ependymal cells is proposed and a role of niacin in ependymal cells highlighted.

Single-Cell Molecular Barcoding to Decode Multimodal Information Defining Cell States

Single-cell research has provided a breakthrough in biology to understand heterogeneous cell groups, such as tissues and organs, in development and disease. Molecular barcoding and subsequent sequencing technology insert a singlecell barcode into isolated single cells, allowing separation cell by cell. Given that multimodal information from a cell defines precise cellular states, recent technical advances in methods focus on simultaneously extracting multimodal data recorded in different biological materials (DNA, RNA, protein, etc.). This review summarizes recently developed singlecell multiomics approaches regarding genome, epigenome, and protein profiles with the transcriptome. In particular, we focus on how to anchor or tag molecules from a cell, improve throughputs with sample multiplexing, and record lineages, and we further discuss the future developments of the technology.

Simultaneous Single-Cell Profiling of the Transcriptome and Accessible Chromatin Using SHARE-seq

The ability to analyze the transcriptomic and epigenomic states of individual single cells has in recent years transformed our ability to measure and understand biological processes. Recent advancements have focused on increasing sensitivity and throughput to provide richer and deeper biological insights at the cellular level. The next frontier is the development of multiomic methods capable of analyzing multiple features from the same cell, such as the simultaneous measurement of the transcriptome and the chromatin accessibility of candidate regulatory elements. In this chapter, we discuss and describe SHARE-seq (Simultaneous high-throughput ATAC, and RNA expression with sequencing) for carrying out simultaneous chromatin accessibility and transcriptome measurements in single cells, together with the experimental and analytical considerations for achieving optimal results.

Batch alignment of single-cell transcriptomics data using deep metric learning

scRNA-seq has uncovered previously unappreciated levels of heterogeneity. With the increasing scale of scRNA-seq studies, the major challenge is correcting batch effect and accurately detecting the number of cell types, which is inevitable in human studies. The majority of scRNA-seq algorithms have been specifically designed to remove batch effect firstly and then conduct clustering, which may miss some rare cell types. Here we develop scDML, a deep metric learning model to remove batch effect in scRNA-seq data, guided by the initial clusters and the nearest neighbor information intra and inter batches. Comprehensive evaluations spanning different species and tissues demonstrated that scDML can remove batch effect, improve clustering performance, accurately recover true cell types and consistently outperform popular methods such as Seurat 3, scVI, Scanorama, BBKNN, Harmony et al. Most importantly, scDML preserves subtle cell types in raw data and enables discovery of new cell subtypes that are hard to extract by analyzing each batch individually. We also show that scDML is scalable to large datasets with lower peak memory usage, and we believe that scDML offers a valuable tool to study complex cellular heterogeneity.

Loading Nanoceria Improves Extracellular Vesicle Membrane Integrity and Therapy to Wounds in Aged Mice

Wound healing is a programmed process through which tissue restores its integrity after an injury. Advancing age is a risk factor for delayed cutaneous wound healing; however, ideal therapeutic approaches for aged wound have not been developed yet. By dissecting the harsh microenvironment of aged wound, we propose an integrated chemical and biological strategy to mitigate two main hostile factors including oxidative stress and ischemia. Mesenchymal stem cell-derived extracellular vesicles (EVs) are a rising star in regenerative medicine due to their powerful facilitation in tissue repair and regeneration. However, the fragile lipid membrane limits their function under the oxidative stress microenvironment. Nanoceria is an antioxidative nanozyme; here, we reveal that nanoceria-loaded EVs derived from mesenchymal stem cells facilitate cutaneous wound healing in aged mice. DG-CeO₂ was prepared via coating CeO₂ covalently with d-glucose to promote their cellular endocytosis. DG-CeO₂ was packaged into EVs under optimized hypoxic conditions (DG-CeO₂ EVs^Hyp). We further demonstrated that DG-CeO₂ EVs^Hyp had favorable biocompatibility and antioxidative and proangiogenic effects during the cutaneous wound healing in both young and aged mice. Further evidence revealed that DG-CeO₂ EVs^Hyp-transferred miR-92a-3p/125b-5p and their targets associated with aging degeneration may be the potential mechanisms. Collectively, these findings highlight that nanoceria-loaded EVs released by engineered stem cells may represent a potential therapeutic approach for tissue regeneration in aged population.

Lung Cell Atlases in Health and Disease

The human lung cellular portfolio, traditionally characterized by cellular morphology and individual markers, is highly diverse, with over 40 cell types and a complex branching structure highly adapted for agile airflow and gas exchange. While constant during adulthood, lung cellular content changes in response to exposure, injury, and infection. Some changes are temporary, but others are persistent, leading to structural changes and progressive lung disease. The recent advance of single-cell profiling technologies allows an unprecedented level of detail and scale to cellular measurements, leading to the rise of comprehensive cell atlas styles of reporting. In this review, we chronical the rise of cell atlases and explore their contributions to human lung biology in health and disease.

Mostly natural sequencing-by-synthesis for scRNA-seq using Ultima sequencing

Here we introduce a mostly natural sequencing-by-synthesis (mnSBS) method for single-cell RNA sequencing (scRNA-seq), adapted to the Ultima genomics platform, and systematically benchmark it against current scRNA-seq technology. mnSBS uses mostly natural, unmodified nucleotides and only a low fraction of fluorescently labeled nucleotides, which allows for high polymerase processivity and lower costs. We demonstrate successful application in four scRNA-seq case studies of different technical and biological types, including 5' and 3' scRNA-seq, human peripheral blood mononuclear cells from a single individual and in multiplex, as well as Perturb-Seq. Benchmarking shows that results from mnSBS-based scRNA-seq are very similar to those using Illumina sequencing, with minor differences in results related to the position of reads relative to annotated gene boundaries, owing to single-end reads of Ultima being closer to gene ends than reads from Illumina. The method is thus compatible with state-of-the-art scRNA-seq libraries independent of the sequencing technology. We expect mnSBS to be of particular utility for cost-effective large-scale scRNA-seq projects.

Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution

The clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic screening has been demonstrated as a powerful approach for unbiased functional genomics research. Single-cell CRISPR screening (scCRISPR) techniques, which result from the combination of single-cell toolkits and CRISPR screening, allow dissecting regulatory networks in complex biological systems at unprecedented resolution. These methods allow cells to be perturbed en masse using a pooled CRISPR library, followed by high-content phenotyping. This is technically accomplished by annotating cells with sgRNA-specific barcodes or directly detectable sgRNAs. According to the integration of distinct single-cell technologies, these methods principally fall into four categories: scCRISPR with RNA-seq, scCRISPR with ATAC-seq, scCRISPR with proteome probing, and imaging-based scCRISPR. scCRISPR has deciphered genotype-phenotype relationships, genetic regulations, tumor biological issues, and neuropathological mechanisms. This review provides insight into the technical breakthrough of scCRISPR by systematically summarizing the advancements of various scCRISPR methodologies and analyzing their merits and limitations. In addition, an application-oriented approach guide is offered to meet researchers' individualized demands.

The end of the beginning: application of single-cell sequencing to chronic lymphocytic leukemia

Single-cell analysis has emerged over the past decade as a transformative technology informative for the systematic analysis of complex cell populations such as in cancers and the tumor immune microenvironment. The methodologic and analytical advancements in this realm have evolved rapidly, scaling from but a few cells at its outset to the current capabilities of processing and analyzing hundreds of thousands of individual cells at a time. The types of profiling attainable at individual cell resolution now range from genetic and transcriptomic characterization and extend to epigenomic and spatial analysis. Additionally, the increasing ability to achieve multiomic integration of these data layers now yields ever richer insights into diverse molecular disease subtypes and the patterns of cellular circuitry on a per-cancer basis. Over the years, chronic lymphocytic leukemia (CLL) consistently has been at the forefront of genomic investigation, given the ready accessibility of pure leukemia cells and immune cells from circulating blood of patients with this disease. Herein, we review the recent forays into the application of single-cell analysis to CLL, which are already revealing a new understanding of the natural progression of CLL, the impact of novel therapies, and the interactions with coevolving nonmalignant immune cell populations. As we emerge from the end of the beginning of this technologic revolution, CLL stands poised to reap the benefits of single-cell analysis from the standpoints of uncovering fresh fundamental biological knowledge and of providing a path to devising regimens of personalized diagnosis, treatment, and monitoring.

Wdr4 promotes cerebellar development and locomotion through Arhgap17-mediated Rac1 activation

Patients with mutations of WDR4, a substrate adaptor of the CUL4 E3 ligase complex, develop cerebellar atrophy and gait phenotypes. However, the underlying mechanisms remain unexplored. Here, we identify a crucial role of Wdr4 in cerebellar development. Wdr4 deficiency in granule neuron progenitors (GNPs) not only reduces foliation and the sizes of external and internal granular layers but also compromises Purkinje neuron organization and the size of the molecular layer, leading to locomotion defects. Mechanistically, Wdr4 supports the proliferation of GNPs by preventing their cell cycle exit. This effect is mediated by Wdr4-induced ubiquitination and degradation of Arhgap17, thereby activating Rac1 to facilitate cell cycle progression. Disease-associated Wdr4 variants, however, cannot provide GNP cell cycle maintenance. Our study identifies Wdr4 as a previously unappreciated participant in cerebellar development and locomotion, providing potential insights into treatment strategies for diseases with WDR4 mutations, such as primordial dwarfism and Galloway-Mowat syndrome.

scm6A-seq reveals single-cell landscapes of the dynamic m6A during oocyte maturation and early embryonic development

N⁶-methyladenosine (m⁶A) has been demonstrated to regulate RNA metabolism and various biological processes, including gametogenesis and embryogenesis. However, the landscape and function of m⁶A at single cell resolution have not been extensively studied in mammalian oocytes or during pre-implantation. In this study, we developed a single-cell m⁶A sequencing (scm⁶A-seq) method to simultaneously profile the m⁶A methylome and transcriptome in single oocytes/blastomeres of cleavage-stage embryos. We found that m⁶A deficiency leads to aberrant RNA clearance and consequent low quality of Mettl3^Gdf9 conditional knockout (cKO) oocytes. We further revealed that m⁶A regulates the translation and stability of modified RNAs in metaphase II (MII) oocytes and during oocyte-to-embryo transition, respectively. Moreover, we observed m⁶A-dependent asymmetries in the epi-transcriptome between the blastomeres of two-cell embryo. scm⁶A-seq thus allows in-depth investigation into m⁶A characteristics and functions, and the findings provide invaluable single-cell resolution resources for delineating the underlying mechanism for gametogenesis and early embryonic development.

Transformer for one stop interpretable cell type annotation

Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated. A series of useful single cell analysis tools based on autoencoder architecture have been developed but these struggle to strike a balance between depth and interpretability. Here, we present TOSICA, a multi-head self-attention deep learning model based on Transformer that enables interpretable cell type annotation using biologically understandable entities, such as pathways or regulons. We show that TOSICA achieves fast and accurate one-stop annotation and batch-insensitive integration while providing biologically interpretable insights for understanding cellular behavior during development and disease progressions. We demonstrate TOSICA's advantages by applying it to scRNA-seq data of tumor-infiltrating immune cells, and CD14+ monocytes in COVID-19 to reveal rare cell types, heterogeneity and dynamic trajectories associated with disease progression and severity.

PRC2 disruption in cerebellar progenitors produces cerebellar hypoplasia and aberrant myoid differentiation without blocking medulloblastoma growth

We show that Polycomb Repressive Complex-2 (PRC2) components EED and EZH2 maintain neural identity in cerebellar granule neuron progenitors (CGNPs) and SHH-driven medulloblastoma, a cancer of CGNPs. Proliferating CGNPs and medulloblastoma cells inherit neural fate commitment through epigenetic mechanisms. The PRC2 is an epigenetic regulator that has been proposed as a therapeutic target in medulloblastoma. To define PRC2 function in cerebellar development and medulloblastoma, we conditionally deleted PRC2 components Eed or Ezh2 in CGNPs and analyzed medulloblastomas induced in Eed-deleted and Ezh2-deleted CGNPs by expressing SmoM2, an oncogenic allele of Smo. Eed deletion destabilized the PRC2, depleting EED and EZH2 proteins, while Ezh2 deletion did not deplete EED. Eed-deleted cerebella were hypoplastic, with reduced proliferation, increased apoptosis, and inappropriate muscle-like differentiation. Ezh2-deleted cerebella showed similar, milder phenotypes, with fewer muscle-like cells and without reduced growth. Eed-deleted and Ezh2-deleted medulloblastomas both demonstrated myoid differentiation and progressed more rapidly than PRC2-intact controls. The PRC2 thus maintains neural commitment in CGNPs and medulloblastoma, but is not required for SHH medulloblastoma progression. Our data define a role for the PRC2 in preventing inappropriate, non-neural fates during postnatal neurogenesis, and caution that targeting the PRC2 in SHH medulloblastoma may not produce durable therapeutic effects.

RNA cytometry of single-cells using semi-permeable microcapsules

Analytical tools for gene expression profiling of individual cells are critical for studying complex biological systems. However, the techniques enabling rapid measurements of gene expression on thousands of single-cells are lacking. Here, we report a high-throughput RNA cytometry for digital profiling of single-cells isolated in liquid droplets enveloped by a thin semi-permeable membrane (microcapsules). Due to the selective permeability of the membrane, the desirable enzymes and reagents can be loaded, or replaced, in the microcapsule at any given step by simply changing the reaction buffer in which the microcapsules are dispersed. Therefore, complex molecular biology workflows can be readily adapted to conduct nucleic acid analysis on encapsulated mammalian cells, or other biological species. The microcapsules support sequential multi-step enzymatic reactions and remain intact under different biochemical conditions, freezing, thawing, and thermocycling. Combining microcapsules with conventional FACS provides a high-throughput approach for conducting RNA cytometry of individual cells based on their digital gene expression signature.

Single-nucleus profiling of adult mice sub-ventricular zone after blast-related traumatic brain injury

Explosive blast-related traumatic brain injuries (bTBI) are common in war zones and urban terrorist attacks. These bTBIs often result in complex neuropathologic damage and neurologic complications. However, there is still a lack of specific strategies for diagnosing and/or treating bTBIs. The sub-ventricular zone (SVZ), which undergoes adult neurogenesis, is critical for the neurological maintenance and repair after brain injury. However, the cellular responses and mechanisms that trigger and modulate these activities in the pathophysiological processes following bTBI remain poorly understood. Here we employ single-nucleus RNA-sequencing (snRNA-seq) of the SVZ from mice subjected to a bTBI. This data-set, including 15272 cells (7778 bTBI and 7494 control) representing all SVZ cell types and is ideally suited for exploring the mechanisms underlying the pathogenesis of bTBIs. Additionally, it can serve as a reference for future studies regarding the diagnosis and treatment of bTBIs.

Single-cell omics: A new direction for functional genetic research in human diseases and animal models

Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.

A universal sequencing read interpreter

Massively parallel DNA sequencing has led to the rapid growth of highly multiplexed experiments in biology. These experiments produce unique sequencing results that require specific analysis pipelines to decode highly structured reads. However, no versatile framework that interprets sequencing reads to extract their encoded information for downstream biological analysis has been developed. Here, we report INTERSTELLAR (interpretation, scalable transformation, and emulation of large-scale sequencing reads) that decodes data values encoded in theoretically any type of sequencing read and translates them into sequencing reads of another structure of choice. We demonstrated that INTERSTELLAR successfully extracted information from a range of short- and long-read sequencing reads and translated those of single-cell (sc)RNA-seq, scATAC-seq, and spatial transcriptomics to be analyzed by different software tools that have been developed for conceptually the same types of experiments. INTERSTELLAR will greatly facilitate the development of sequencing-based experiments and sharing of data analysis pipelines.

SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data

MOTIVATION

Integrative analysis of multiple single-cell RNA-sequencing datasets allows for more comprehensive characterizations of cell types, but systematic technical differences between datasets, known as 'batch effects', need to be removed before integration to avoid misleading interpretation of the data. Although many batch-effect-removal methods have been developed, there is still a large room for improvement: most existing methods only give dimension-reduced data instead of expression data of individual genes, are based on computationally demanding models and are black-box models and thus difficult to interpret or tune.

RESULTS

Here, we present a new batch-effect-removal method called SCIBER (Single-Cell Integrator and Batch Effect Remover) and study its performance on real datasets. SCIBER matches cell clusters across batches according to the overlap of their differentially expressed genes. As a simple algorithm that has better scalability to data with a large number of cells and is easy to tune, SCIBER shows comparable and sometimes better accuracy in removing batch effects on real datasets compared to the state-of-the-art methods, which are much more complicated. Moreover, SCIBER outputs expression data in the original space, that is, the expression of individual genes, which can be used directly for downstream analyses. Additionally, SCIBER is a reference-based method, which assigns one of the batches as the reference batch and keeps it untouched during the process, making it especially suitable for integrating user-generated datasets with standard reference data such as the Human Cell Atlas.

AVAILABILITY AND IMPLEMENTATION

SCIBER is publicly available as an R package on CRAN: https://cran.r-project.org/web/packages/SCIBER/. A vignette is included in the CRAN R package.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Current and future perspectives of single-cell multi-omics technologies in cardiovascular research

Single-cell technology has become an indispensable tool in cardiovascular research since its first introduction in 2009. Here, we highlight the recent remarkable progress in using single-cell technology to study transcriptomic and epigenetic heterogeneity in cardiac disease and development. We then introduce the key concepts in single-cell multi-omics modalities that apply to cardiovascular research. Lastly, we discuss some of the trending concepts in single-cell technology that are expected to propel cardiovascular research to the next phase of single-cell research.

Preparation of Drosophila Ovarioles for Single-Cell RNA Sequencing

The production of eggs in the Drosophila ovary requires complex interactions between multiple cell types that coexist within the same solid tissue. This cellular heterogeneity makes the ovary a rich subject of study, but also makes it challenging to identify transcriptional differences between individual cell types using methods such as bulk RNA sequencing. The development of single-cell RNA sequencing (scRNA-seq) techniques addresses this limitation by providing an avenue to profile genetic and functional heterogeneity at a cellular resolution. Here, we describe the isolation and preparation of the Drosophila ovary for scRNA-seq. This protocol emphasizes a short preparation time, high cell viability, prevention of RNA-degradation, and reduction of technical variation to achieve highly reproducible single-cell profiles.

scRNA-seq for Microcephaly Research [III]: Computational Analysis of scRNA-seq Data

Single-cell transcriptomic analysis (scRNA-seq) can enable researchers to explore the gene expression patterns of thousands of individual cells simultaneously. Processing the complex data generated by scRNA-seq requires specialized computational tools. This chapter focuses on the analytical aspect of scRNA-seq workflow, with a focus on resolving biological signals from large-scale scRNA-seq data produced by the Drop-Seq platform.

Recent advances in understanding neuronal diversity and neural circuit complexity across different brain regions using single-cell sequencing

Neural circuits are characterized as interconnecting neuron networks connected by synapses. Some kinds of gene expression and/or functional changes of neurons and synaptic connections may result in aberrant neural circuits, which has been recognized as one crucial pathological mechanism for the onset of many neurological diseases. Gradual advances in single-cell sequencing approaches with strong technological advantages, as exemplified by high throughput and increased resolution for live cells, have enabled it to assist us in understanding neuronal diversity across diverse brain regions and further transformed our knowledge of cellular building blocks of neural circuits through revealing numerous molecular signatures. Currently published transcriptomic studies have elucidated various neuronal subpopulations as well as their distribution across prefrontal cortex, hippocampus, hypothalamus, and dorsal root ganglion, etc. Better characterization of brain region-specific circuits may shed light on new pathological mechanisms involved and assist in selecting potential targets for the prevention and treatment of specific neurological disorders based on their established roles. Given diverse neuronal populations across different brain regions, we aim to give a brief sketch of current progress in understanding neuronal diversity and neural circuit complexity according to their locations. With the special focus on the application of single-cell sequencing, we thereby summarize relevant region-specific findings. Considering the importance of spatial context and connectivity in neural circuits, we also discuss a few published results obtained by spatial transcriptomics. Taken together, these single-cell sequencing data may lay a mechanistic basis for functional identification of brain circuit components, which links their molecular signatures to anatomical regions, connectivity, morphology, and physiology. Furthermore, the comprehensive characterization of neuron subtypes, their distributions, and connectivity patterns via single-cell sequencing is critical for understanding neural circuit properties and how they generate region-dependent interactions in different context.

Optimized single-nucleus transcriptional profiling by combinatorial indexing

Single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) is a powerful method for recovering gene expression data from an exponentially scalable number of individual cells or nuclei. However, sci-RNA-seq is a complex protocol that has historically exhibited variable performance on different tissues, as well as lower sensitivity than alternative methods. Here, we report a simplified, optimized version of the sci-RNA-seq protocol with three rounds of split-pool indexing that is faster, more robust and more sensitive and has a higher yield than the original protocol, with reagent costs on the order of 1 cent per cell or less. The total hands-on time from nuclei isolation to final library preparation takes 2-3 d, depending on the number of samples sharing the experiment. The improvements also allow RNA profiling from tissues rich in RNases like older mouse embryos or adult tissues that were problematic for the original method. We showcase the optimized protocol via whole-organism analysis of an E16.5 mouse embryo, profiling ~380,000 nuclei in a single experiment. Finally, we introduce a 'Tiny-Sci' protocol for experiments in which input material is very limited.

Into the multi-omics era: Progress of T cells profiling in the context of solid organ transplantation

T cells are the common type of lymphocyte to mediate allograft rejection, remaining long-term allograft survival impeditive. However, the heterogeneity of T cells, in terms of differentiation and activation status, the effector function, and highly diverse T cell receptors (TCRs) have thus precluded us from tracking these T cells and thereby comprehending their fate in recipients due to the limitations of traditional detection approaches. Recently, with the widespread development of single-cell techniques, the identification and characterization of T cells have been performed at single-cell resolution, which has contributed to a deeper comprehension of T cell heterogeneity by relevant detections in a single cell - such as gene expression, DNA methylation, chromatin accessibility, surface proteins, and TCR. Although these approaches can provide valuable insights into an individual cell independently, a comprehensive understanding can be obtained when applied joint analysis. Multi-omics techniques have been implemented in characterizing T cells in health and disease, including transplantation. This review focuses on the thesis, challenges, and advances in these technologies and highlights their application to the study of alloreactive T cells to improve the understanding of T cell heterogeneity in solid organ transplantation.

Integrative genetic and single cell RNA sequencing analysis provides new clues to the amyotrophic lateral sclerosis neurodegeneration

Introduction

The gradual loss of motor neurons (MNs) in the brain and spinal cord is a hallmark of amyotrophic lateral sclerosis (ALS), but the mechanisms underlying neurodegeneration in ALS are still not fully understood.

Methods

Based on 75 ALS-pathogenicity/susceptibility genes and large-scale single-cell transcriptomes of human/mouse brain/spinal cord/muscle tissues, we performed an expression enrichment analysis to identify cells involved in ALS pathogenesis. Subsequently, we created a strictness measure to estimate the dosage requirement of ALS-related genes in linked cell types.

Results

Remarkably, expression enrichment analysis showed that α- and γ-MNs, respectively, are associated with ALS-susceptibility genes and ALS-pathogenicity genes, revealing differences in biological processes between sporadic and familial ALS. In MNs, ALS-susceptibility genes exhibited high strictness, as well as the ALS-pathogenicity genes with known loss of function mechanism, indicating the main characteristic of ALS-susceptibility genes is dosage-sensitive and the loss of function mechanism of these genes may involve in sporadic ALS. In contrast, ALS-pathogenicity genes with gain of function mechanism exhibited low strictness. The significant difference of strictness between loss of function genes and gain of function genes provided a priori understanding for the pathogenesis of novel genes without an animal model. Besides MNs, we observed no statistical evidence for an association between muscle cells and ALS-related genes. This result may provide insight into the etiology that ALS is not within the domain of neuromuscular diseases. Moreover, we showed several cell types linked to other neurological diseases [i.e., spinocerebellar ataxia (SA), hereditary motor neuropathies (HMN)] and neuromuscular diseases [i.e. hereditary spastic paraplegia (SPG), spinal muscular atrophy (SMA)], including an association between Purkinje cells in brain and SA, an association between α-MNs in spinal cord and SA, an association between smooth muscle cells and SA, an association between oligodendrocyte and HMN, a suggestive association between γ-MNs and HMN, a suggestive association between mature skeletal muscle and HMN, an association between oligodendrocyte in brain and SPG, and no statistical evidence for an association between cell type and SMA.

Discussion

These cellular similarities and differences deepened our understanding of the heterogeneous cellular basis of ALS, SA, HMN, SPG, and SMA.

Single-Cell Joint Profiling of Open Chromatin and Transcriptome by Paired-Seq

Simultaneous detection of chromatin accessibility and transcription from the same cells promises to greatly facilitate the dissection of cell-type-specific gene regulatory programs in complex tissues. Paired-seq enables joint analysis of open chromatin and nuclear transcriptome from up to a million cells in parallel. It achieves ultra-high-throughput single-cell multiomics with the use of a combinatorial barcoding strategy involving sequential ligation of multiplexed DNA barcodes to chromatin DNA fragments and reverse transcription products, followed by high-throughput DNA sequencing of the resulting DNA libraries and deconvolution of single-cell multiomic maps based on cell-specific barcodes.

Bulk and Single-Cell RNA-Seq Analyses for Studies of Spermatogonia

Robust methods have been developed that leverage next-generation sequencing (NGS) to measure abundance of all mRNAs (RNA-seq) in samples as small as individual cells in order to study the testicular transcriptome in mammals. In this chapter, we present robust options for implementing bioinformatics workflows for the analysis of bulk RNA-seq from aggregate samples of hundreds to millions of cells and single-cell RNA-seq from individual cells. We also provide detailed protocols for using the R packages DESeq2 and Seurat, important parameters for successful implementation, and considerations for drawing conclusions from the results.

scRNA-seq for Microcephaly Research [I]: Single-Cell Droplet Encapsulation, mRNA Capture, and cDNA Synthesis

Single-cell RNA sequencing (scRNA-seq) allows for the transcriptomic profiling of a sample tissue with single-cell resolution. The concept of scRNA-seq builds on traditional, "bulk" RNA-seq by recording and preserving the cellular origin of each transcript throughout library preparation. Here we describe an adaptation of the Drop-Seq method (Macosko et al. Cell 161, 1202-1214, 2015), in which nanoliter-scale droplets are used to physically separate dissociated cells, while a cell-specific DNA barcode is simultaneously introduced. Following barcoding, cDNAs can be mixed and pooled while retaining the identity of the cell of origin. The benefit of the Drop-Seq approach is high throughput from relatively small samples of tissue. The method described here is appropriate for processing an input of as few as 150,000 cells, with a final yield of as many as 5000 single-cell transcripts captured.

Single-cell RNA-seq methods to interrogate virus-host interactions

The twenty-first century has seen the emergence of many epidemic and pandemic viruses, with the most recent being the SARS-CoV-2-driven COVID-19 pandemic. As obligate intracellular parasites, viruses rely on host cells to replicate and produce progeny, resulting in complex virus and host dynamics during an infection. Single-cell RNA sequencing (scRNA-seq), by enabling broad and simultaneous profiling of both host and virus transcripts, represents a powerful technology to unravel the delicate balance between host and virus. In this review, we summarize technological and methodological advances in scRNA-seq and their applications to antiviral immunity. We highlight key scRNA-seq applications that have enabled the understanding of viral genomic and host response heterogeneity, differential responses of infected versus bystander cells, and intercellular communication networks. We expect further development of scRNA-seq technologies and analytical methods, combined with measurements of additional multi-omic modalities and increased availability of publicly accessible scRNA-seq datasets, to enable a better understanding of viral pathogenesis and enhance the development of antiviral therapeutics strategies.

Graded spikes differentially signal neurotransmitter input in cerebrospinal fluid contacting neurons of the mouse spinal cord

The action potential and its all-or-none nature is fundamental to neural communication. Canonically, the action potential is initiated once voltage-activated Na⁺ channels are activated, and their rapid kinetics of activation and inactivation give rise to the action potential's all-or-none nature. Here we demonstrate that cerebrospinal fluid contacting neurons (CSFcNs) surrounding the central canal of the mouse spinal cord employ a different strategy. Rather than using voltage-activated Na⁺ channels to generate binary spikes, CSFcNs use two different types of voltage-activated Ca²⁺ channel, enabling spikes of different amplitude. T-type Ca²⁺ channels generate small amplitude spikes, whereas larger amplitude spikes require high voltage-activated Cd²⁺-sensitive Ca²⁺ channels. We demonstrate that these different amplitude spikes can signal input from different transmitter systems; purinergic inputs evoke smaller T-type dependent spikes whereas cholinergic inputs evoke larger spikes that do not rely on T-type channels. Different synaptic inputs to CSFcNs can therefore be signaled by the spike amplitude.

ERR2 and ERR3 promote the development of gamma motor neuron functional properties required for proprioceptive movement control

The ability of terrestrial vertebrates to effectively move on land is integrally linked to the diversification of motor neurons into types that generate muscle force (alpha motor neurons) and types that modulate muscle proprioception, a task that in mammals is chiefly mediated by gamma motor neurons. The diversification of motor neurons into alpha and gamma types and their respective contributions to movement control have been firmly established in the past 7 decades, while recent studies identified gene expression signatures linked to both motor neuron types. However, the mechanisms that promote the specification of gamma motor neurons and/or their unique properties remained unaddressed. Here, we found that upon selective loss of the orphan nuclear receptors ERR2 and ERR3 (also known as ERRβ, ERRγ or NR3B2, NR3B3, respectively) in motor neurons in mice, morphologically distinguishable gamma motor neurons are generated but do not acquire characteristic functional properties necessary for regulating muscle proprioception, thus disrupting gait and precision movements. Complementary gain-of-function experiments in chick suggest that ERR2 and ERR3 could operate via transcriptional activation of neural activity modulators to promote a gamma motor neuron biophysical signature of low firing thresholds and high firing rates. Our work identifies a mechanism specifying gamma motor neuron functional properties essential for the regulation of proprioceptive movement control.

Large-Scale Single-Nucleus RNA Sequencing Compatible with Complex Archived Samples

Transcriptome profiling at single-cell resolution allows us to identify and assess functional cell types and cellular states, including those within degenerating ocular tissues in retinitis pigmentosa. The technology is particularly valuable when studying tissues with high cellular heterogeneity, or when specific cell types are of interest. In this chapter, we introduce a detailed protocol of a medium-throughput single-nucleus RNA sequencing technique that utilizes frozen tissue as input sample. This protocol can be executed by any researcher with basic training in molecular biology techniques. With this protocol, a single experimenter can easily process two samples per day up to cDNA amplification, and library preparations can be done in batches of 8. Routinely we can obtain ~20 K nuclei per eye from 3 to 4 library preparations.

Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis

The cerebral cortex contains billions of neurons, and their disorganization or misspecification leads to neurodevelopmental disorders. Understanding how the plethora of projection neuron subtypes are generated by cortical neural stem cells (NSCs) is a major challenge. Here, we focused on elucidating the transcriptional landscape of murine embryonic NSCs, basal progenitors (BPs), and newborn neurons (NBNs) throughout cortical development. We uncover dynamic shifts in transcriptional space over time and heterogeneity within each progenitor population. We identified signature hallmarks of NSC, BP, and NBN clusters and predict active transcriptional nodes and networks that contribute to neural fate specification. We find that the expression of receptors, ligands, and downstream pathway components is highly dynamic over time and throughout the lineage implying differential responsiveness to signals. Thus, we provide an expansive compendium of gene expression during cortical development that will be an invaluable resource for studying neural developmental processes and neurodevelopmental disorders.

Recent advances in high-throughput single-cell transcriptomics and spatial transcriptomics

Single-cell RNA sequencing (scRNA-seq) has been developed for characterizing the transcriptome of cells that are rare but of biological significance. With cell barcoding and microchip technologies, a suite of high-throughput scRNA-seq protocols enable transcriptome profiling in thousands of individual cells at single-cell resolution for classifying cell types, discovering novel cell populations, investigating cellular heterogeneity and elucidating lineage trajectories. Microchip technologies including microfluidics- and microwell-based platforms play a major role in high-throughput scRNA-seq. As the emerging technology, spatial transcriptomics integrates cellular transcriptomics with their spatial coordinates within tissues for spatially deciphering cellular composition, heterogeneity and cell-cell communications. Spatial transcriptomics has been increasingly recognized as one of the most powerful tools for discovering new biology and advancing precision medicine. Microfluidics as an enabling technology plays an increasingly important role in spatial transcriptomics. We review the technological spectrum and advances in high-throughput scRNA-seq and spatial transcriptomics, discuss their advantages and limitations, and pitch into new biology learned from these new tools.

W, Chen H, Yu C, Yang L, Fu Y, Li J, Fu S, Sun Z, Fei L, Guo Q, Wang J, Xiao Y, Wang X, Zhang P, Ma L, Ge D, Xu S, Caballero-Pérez J, Cruz-Ramírez A, Zhou Y, Chen M, Fei JF, Han X, Guo G. Construction of the axolotl cell landscape using combinatorial hybridization sequencing at single-cell resolution

The Mexican axolotl (Ambystoma mexicanum) is a well-established tetrapod model for regeneration and developmental studies. Remarkably, neotenic axolotls may undergo metamorphosis, a process that triggers many dramatic changes in diverse organs, accompanied by gradually decline of their regeneration capacity and lifespan. However, the molecular regulation and cellular changes in neotenic and metamorphosed axolotls are still poorly investigated. Here, we develop a single-cell sequencing method based on combinatorial hybridization to generate a tissue-based transcriptomic landscape of the neotenic and metamorphosed axolotls. We perform gene expression profiling of over 1 million single cells across 19 tissues to construct the first adult axolotl cell landscape. Comparison of single-cell transcriptomes between the tissues of neotenic and metamorphosed axolotls reveal the heterogeneity of non-immune parenchymal cells in different tissues and established their regulatory network. Furthermore, we describe dynamic gene expression patterns during limb development in neotenic axolotls. This system-level single-cell analysis of molecular characteristics in neotenic and metamorphosed axolotls, serves as a resource to explore the molecular identity of the axolotl and facilitates better understanding of metamorphosis.

The Mexican axolotl is a well-established tetrapod model for regeneration and development. Here the authors report a scRNA-seq method to profile neotenic, metamorphic and limb development stages, highlighting unique perturbation patterns of cell type-related gene expression throughout metamorphosis.

Single-cell RNA sequencing for traumatic spinal cord injury

Traumatic spinal cord injury (tSCI) is a severe injury of the central nervous system (CNS) with complicated pathological microenvironment that results in hemorrhage, inflammation, and scar formation. The microenvironment of the injured spinal cord comprises heterogeneous neurons, glial cells, inflammatory cells, and stroma-related cells. Increasing evidence has indicated that the altered cellular and molecular microenvironment following tSCI is a key factor impeding functional recovery. Single-cell RNA sequencing (scRNA-seq) has provided deep insights into the dynamic cellular and molecular changes in the microenvironment by comprehensively characterizing the diversity of spinal cord cell types. Specifically, scRNA-seq enables the exploration of the molecular mechanisms underlying tSCI by elucidating intercellular communication in spinal cord samples between normal and injury conditions at a single-cell resolution. Here, we first described the pathological and physiological processes after tSCI and gave a brief introduction of the scRNA-seq technology. We then focused on the recent scRNA-seq researches in tSCI, which characterized diverse cell-type populations and specific cell-cell interactions in tSCI. In addition, we also highlighted some potential directions for the research of scRNA-seq in tSCI in the future.

Profiling of bacterial transcriptome from ultra-low input with MiniBac-seq

There is a lack of appropriate methods for preparing bacterial RNA-seq library with ultra-low amount of RNA. To address this issue, we developed miniBac-seq, a strand-specific method for high-quality library construction from sub-nanogram of total RNA, which is 100-fold lower than the current benchmark kit and dramatically reduces preparation cost ($28 + $15 × samples). We further demonstrated the high sensitivity of miniBac-seq via detecting more than 500 genes from amount of total RNA equivalent to that of a single bacterial cell. Finally, we profiled the transcriptome of growth-arrested bacteria in isogenic culture of Escherichia coli. This subpopulation of bacteria is generally low in abundance but is a potent reservoir of antibiotic persistence, and their gene expression has been largely unknown due to technical limitations. Using miniBac-seq, we identified potential molecular driver towards arrested growth as well as antibiotic tolerance. Our method thus expands the capacity to quantify bacterial transcriptome in situ, which is useful to the understanding of bacterial physiology and regulation in their native contexts.

Research progress of single-cell transcriptome sequencing in autoimmune diseases and autoinflammatory disease: A review

Autoimmunity refers to the phenomenon that the body's immune system produces antibodies or sensitized lymphocytes to its own tissues to cause an immune response. Immune disorders caused by autoimmunity can mediate autoimmune diseases. Autoimmune diseases have complicated pathogenesis due to the many types of cells involved, and the mechanism is still unclear. The emergence of single-cell research technology can solve the problem that ordinary transcriptome technology cannot be accurate to cell type. It provides unbiased results through independent analysis of cells in tissues and provides more mRNA information for identifying cell subpopulations, which provides a novel approach to study disruption of immune tolerance and disturbance of pro-inflammatory pathways on a cellular basis. It may fundamentally change the understanding of molecular pathways in the pathogenesis of autoimmune diseases and develop targeted drugs. Single-cell transcriptome sequencing (scRNA-seq) has been widely applied in autoimmune diseases, which provides a powerful tool for demonstrating the cellular heterogeneity of tissues involved in various immune inflammations, identifying pathogenic cell populations, and revealing the mechanism of disease occurrence and development. This review describes the principles of scRNA-seq, introduces common sequencing platforms and practical procedures, and focuses on the progress of scRNA-seq in 41 autoimmune diseases, which include 9 systemic autoimmune diseases and autoinflammatory diseases (rheumatoid arthritis, systemic lupus erythematosus, etc.) and 32 organ-specific autoimmune diseases (5 Skin diseases, 3 Nervous system diseases, 4 Eye diseases, 2 Respiratory system diseases, 2 Circulatory system diseases, 6 Liver, Gallbladder and Pancreas diseases, 2 Gastrointestinal system diseases, 3 Muscle, Bones and joint diseases, 3 Urinary system diseases, 2 Reproductive system diseases). This review also prospects the molecular mechanism targets of autoimmune diseases from the multi-molecular level and multi-dimensional analysis combined with single-cell multi-omics sequencing technology (such as scRNA-seq, Single cell ATAC-seq and single cell immune group library sequencing), which provides a reference for further exploring the pathogenesis and marker screening of autoimmune diseases and autoimmune inflammatory diseases in the future.

Effects of metformin on modulating the expression of brain-related genes of APP/PS1 transgenic mice based on Single Cell Sequencing

BACKGROUND

Alzheimer's disease is the most common form of dementia, affecting millions of people worldwide.

METHODS

Here, we analyzed the effects of metformin on APP/PS1 transgenic mice by behavioral test and single-cell sequencing. Results showed that metformin can improve the spatial learning, memory function, and anxiety mood of APP/PS1 transgenic mice. We identified transcriptionally distinct subpopulations of nine major brain cell types. Metformin increased the differentiation of stem cells, decreased the proportion of cells in the G2 phase, enhanced the generation of neural stem cells and oligodendrocyte progenitor cells, and the tendency of neural stem cells to differentiate into astrocytes. Notably, 253 genes expressed abnormally in APP/PS1 transgenic mice and were reversed by metformin. Ttr, Uba52, and Rps21 are the top 3 genes in the cell-gene network with the highest node degree. Moreover, histochemistry showed the expressions of RPS15, UBA52, and RPL23a were consistent with the data from single-cell sequencing. Pathway and biological process enrichment analysis indicated metformin was involved in nervous system development and negative regulation of the apoptotic process.

CONCLUSION

Overall, metformin might play an important role in the differentiation and development and apoptotic process of the central nervous system by regulating the expression of Ttr, Uba52, Rps21, and other genes to improve cognition of APP/PS1 transgenic mice. These results provided a clue for elaborating on the molecular and cellular basis of metformin on AD.

Single-cell transcriptional and functional analysis of dopaminergic neurons in organoid-like cultures derived from human fetal midbrain

Significant efforts are ongoing to develop refined differentiation protocols to generate midbrain dopamine (DA) neurons from pluripotent stem cells for application in disease modeling, diagnostics, drug screening and cell-based therapies for Parkinson's disease. An increased understanding of the timing and molecular mechanisms that promote the generation of distinct subtypes of human midbrain DA during development will be essential for guiding future efforts to generate molecularly defined and subtype-specific DA neurons from pluripotent stem cells. Here, we use droplet-based single-cell RNA sequencing to transcriptionally profile the developing human ventral midbrain (VM) when the DA neurons are generated (6-11 weeks post-conception) and their subsequent differentiation into functional mature DA neurons in primary fetal 3D organoid-like cultures. This approach reveals that 3D cultures are superior to monolayer conditions for their ability to generate and maintain mature DA neurons; hence, they have the potential to be used for studying human VM development. These results provide a unique transcriptional profile of the developing human fetal VM and functionally mature human DA neurons that can be used to guide stem cell-based therapies and disease modeling approaches in Parkinson's disease.

Applications of single-cell RNA sequencing in atopic dermatitis and psoriasis

Single-cell RNA sequencing (scRNA-seq) is a novel technology that characterizes molecular heterogeneity at the single-cell level. With the development of more automated, sensitive, and cost-effective single-cell isolation methods, the sensitivity and efficiency of scRNA-seq have improved. Technological advances in single-cell analysis provide a deeper understanding of the biological diversity of cells present in tissues, including inflamed skin. New subsets of cells have been discovered among common inflammatory skin diseases, such as atopic dermatitis (AD) and psoriasis. ScRNA-seq technology has also been used to analyze immune cell distribution and cell-cell communication, shedding new light on the complex interplay of components involved in disease responses. Moreover, scRNA-seq may be a promising tool in precision medicine because of its ability to define cell subsets with potential treatment targets and to characterize cell-specific responses to drugs or other stimuli. In this review, we briefly summarize the progress in the development of scRNA-seq technologies and discuss the latest scRNA-seq-related findings and future trends in AD and psoriasis. We also discuss the limitations and technical problems associated with current scRNA-seq technology.

Advances in Single-Cell Sequencing Technology and Its Application in Poultry Science

Single-cell sequencing (SCS) uses a single cell as the research material and involves three dimensions: genes, phenotypes and cell biological mechanisms. This type of research can locate target cells, analyze the dynamic changes in the target cells and the relationships between the cells, and pinpoint the molecular mechanism of cell formation. Currently, a common problem faced by animal husbandry scientists is how to apply existing science and technology to promote the production of high-quality livestock and poultry products and to breed livestock for disease resistance; this is also a bottleneck for the sustainable development of animal husbandry. In recent years, although SCS technology has been successfully applied in the fields of medicine and bioscience, its application in poultry science has been rarely reported. With the sustainable development of science and technology and the poultry industry, SCS technology has great potential in the application of poultry science (or animal husbandry). Therefore, it is necessary to review the innovation of SCS technology and its application in poultry science. This article summarizes the current main technical methods of SCS and its application in poultry, which can provide potential references for its future applications in precision breeding, disease prevention and control, immunity, and cell identification.

Spatially aware dimension reduction for spatial transcriptomics

Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex.