Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems

Barcodes based on nucleic acid sequences: Applications and challenges (Review)

Cells are the fundamental structural and functional units of living organisms and the study of these entities has remained a central focus throughout the history of biological sciences. Traditional cell research techniques, including fluorescent protein tagging and microscopy, have provided preliminary insights into the lineage history and clonal relationships between progenitor and descendant cells. However, these techniques exhibit inherent limitations in tracking the full developmental trajectory of cells and elucidating their heterogeneity, including sensitivity, stability and barcode drift. In developmental biology, nucleic acid barcode technology has introduced an innovative approach to cell lineage tracing. By assigning unique barcodes to individual cells, researchers can accurately identify and trace the origin and differentiation pathways of cells at various developmental stages, thereby illuminating the dynamic processes underlying tissue development and organogenesis. In cancer research, nucleic acid barcoding has played a pivotal role in analyzing the clonal architecture of tumor cells, exploring their heterogeneity and resistance mechanisms and enhancing our understanding of cancer evolution and inter‑clonal interactions. Furthermore, nucleic acid barcodes play a crucial role in stem cell research, enabling the tracking of stem cells from diverse origins and their derived progeny. This has offered novel perspectives on the mechanisms of stem cell self‑renewal and differentiation. The present review presented a comprehensive examination of the principles, applications and challenges associated with nucleic acid barcode technology.

Single-cell technology for cell-based drug delivery and pharmaceutical research

Leveraging the capacity to precisely manipulate and analyze individual cells, single-cell technology has rapidly become an indispensable tool in the advancement of cell-based drug delivery systems and innovative cell therapies. This technology offers powerful means to address cellular heterogeneity and significantly enhance therapeutic efficacy. Recent breakthroughs in techniques such as single-cell electroporation, mechanical perforation, and encapsulation, particularly when integrated with microfluidics and bioelectronics, have led to remarkable improvements in drug delivery efficiency, reductions in cytotoxicity, and more precise targeting of therapeutic effects. Moreover, single-cell analyses, including advanced sequencing and high-resolution sensing, offer profound insights into complex disease mechanisms, the development of drug resistance, and the intricate processes of stem cell differentiation. This review summarizes the most significant applications of these single-cell technologies, highlighting their impact on the landscape of modern biomedicine. Furthermore, it provides a forward-looking perspective on future research directions aimed at further optimizing drug delivery strategies and enhancing therapeutic outcomes in the treatment of various diseases.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics

The development of micro- and nano-fabrication technologies has greatly advanced single-cell and spatial omics technologies. With the advantages of integration and compartmentalization, microfluidic chips are capable of generating high-throughput parallel reaction systems for single-cell screening and analysis. As omics technologies improve, microfluidic chips can now integrate promising transcriptomics technologies, providing new insights from molecular characterization for tissue gene expression profiles and further revealing the static and even dynamic processes of tissues in homeostasis and disease. Here, we survey the current landscape of microfluidic methods in the field of single-cell and spatial multi-omics, as well as assessing their relative advantages and limitations. We highlight how microfluidics has been adapted and improved to provide new insights into multi-omics over the past decade. Last, we emphasize the contributions of microfluidic-based omics methods in development, neuroscience, and disease mechanisms, as well as further revealing some perspectives for technological advances in translational and clinical medicine.

Pulmonary Myeloid Cells in Mild Cases of COVID-19 Upregulate the Intracellular Fc Receptor TRIM21 and Transcribe Proteasome-Associated Molecules

Much remains to be understood about COVID-19, but the protective role of antibodies (Igs) is widely accepted in SARS-CoV-2 infection. Igs' functions are mainly carried out by receptors that bind to their Fc portion (FcR), and less attention has been dedicated to the cytoplasmic members of this family. In this work, we used single-cell RNA sequencing (scRNA-seq) data to discern cell populations in bronchoalveolar lavage fluid obtained from healthy individuals and patients with mild or severe COVID-19. Then, we evaluated the transcription of neonatal FcR (FcRn, FCGRT gene) and tripartite motif-containing protein 21 (TRIM21) and its downstream signaling components. The TRIM21 pathway is vital for virus infections as it has a dual function, leading opsonized viruses to degradation by proteasomes and the activation of innate inflammatory anti-virus response. The transcriptional level of FCGRT showed no statistical differences in any cell population comparing the three groups of patients. On the other hand, TRIM21 transcription was significantly higher in myeloid cells collected from patients with mild COVID-19. When comparing mild with severe cases, there was no statistical difference in TRIM21 transcription in lung adaptive lymphoid cells and innate lymphoid cells (ILC). Yet, we analyzed the transcription of all downstream signaling molecules in myeloid and, as most cells expressed the receptor, in adaptive lymphoid cells. Moreover, ILCs from mild cases and all cell populations from severe cases were missing most downstream components of the pathway. We observed that members of the ubiquitin-proteasome system (UPS) and other components associated with TRIM21 proteasomal degradation were transcribed in mild cases. Despite the transcription of the danger sensors DDX58 and IFIH1, the transcriptional level of inflammatory IL1B and IL18 was generally very low, along with the NLRP3 danger sensor, members of the NF-κB pathway, and TNF. Therefore, our data suggest that TRIM21 may contribute to SARS-CoV-2 protection by reducing the viral load, while the inflammatory branch of the pathway would be silenced, leading to no pathogenic cytokine production.

Prognostic analysis and identification of M7G immune-related genes in lung squamous cell carcinoma

Background

In recent years, the clinical application of targeted therapies and immunotherapy has significantly improved survival outcomes for patients with lung adenocarcinomas(LUAD). However, due to fewer mutations, lung squamous cell carcinomas(LUSC) shows limited efficacy with targeted and immunotherapy, resulting in a notably lower 5-year survival rate compared to lung adenocarcinoma. The m7G modification plays an important role in tumorigenesis, progression, immune evasion, and therapeutic response. This study aims to develop a novel scoring system based on m7G modification and immune status to clinically predict the prognosis of patients with LUSC and to provide new therapeutic targets.

Methods

In this study, we utilized RNA-seq data from the TCGA-LUSC database as the training set and GSE50081 from the GEO database as the validation set. Immunotherapy data were obtained from the IMMPORT database, and m7G data from previous research. Using bioinformatics, we developed a prognostic model for LUSC based on m7G pathway-related immune gene characteristics. We analyzed the correlation between the prognostic model and clinical pathological features of LUSC, as well as the model's independent prognostic capability. Subsequently, patients were divided into high-risk and low-risk groups, and we examined the differences in enriched pathways, immune cell infiltration correlations, and drug sensitivity between the two groups.

Results

The m7G immune-related genes FGA, CSF3R, and ORM1 increase the survival risk in patients with lung squamous cell carcinoma, whereas NTS exerts a protective effect. The prognostic risk model for lung squamous cell carcinoma (LUSC) based on m7G immune-related gene expression demonstrates that the overall survival of the high-risk group is significantly poorer than that of the low-risk group.

Conclusion

The risk model developed based on m7G immune-related genes can help predict the clinical prognosis of LUSC patients and guide treatment decisions.

The landscape of cell lineage tracing

Cell fate changes play a crucial role in the processes of natural development, disease progression, and the efficacy of therapeutic interventions. The definition of the various types of cell fate changes, including cell expansion, differentiation, transdifferentiation, dedifferentiation, reprogramming, and state transitions, represents a complex and evolving field of research known as cell lineage tracing. This review will systematically introduce the research history and progress in this field, which can be broadly divided into two parts: prospective tracing and retrospective tracing. The initial section encompasses an array of methodologies pertaining to isotope labeling, transient fluorescent tracers, non-fluorescent transient tracers, non-fluorescent genetic markers, fluorescent protein, genetic marker delivery, genetic recombination, exogenous DNA barcodes, CRISPR-Cas9 mediated DNA barcodes, and base editor-mediated DNA barcodes. The second part of the review covers genetic mosaicism, genomic DNA alteration, TCR/BCR, DNA methylation, and mitochondrial DNA mutation. In the final section, we will address the principal challenges and prospective avenues of enquiry in the field of cell lineage tracing, with a particular focus on the sequencing techniques and mathematical models pertinent to single-cell genetic lineage tracing, and the value of pursuing a more comprehensive investigation at both the spatial and temporal levels in the study of cell lineage tracing.

Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI

Recent advances in multi-omics and spatially resolved single-cell technologies have revolutionised our ability to profile millions of cellular states, offering unprecedented opportunities to understand the complex molecular landscapes of human tissues in both health and disease. These developments hold immense potential for precision medicine, particularly in the rational design of novel therapeutics for treating inflammatory and autoimmune diseases. However, the vast, high-dimensional data generated by these technologies present significant analytical challenges, such as distinguishing technical variation from biological variation or defining relevant questions that leverage the added spatial dimension to improve our understanding of tissue organisation. Generative artificial intelligence (AI), specifically variational autoencoder- or transformer-based latent variable models, provides a powerful and flexible approach to addressing these challenges. These models make inferences about a cell's intrinsic state by effectively identifying complex patterns, reducing data dimensionality and modelling the biological variability in single-cell datasets. This review explores the current landscape of single-cell and spatial multi-omics technologies, the application of generative AI in data analysis and modelling and their transformative impact on our understanding of autoimmune diseases. By combining spatial and single-cell data with advanced AI methodologies, we highlight novel insights into the pathogenesis of autoimmune disorders and outline future directions for leveraging these technologies to achieve the goal of AI-powered personalised medicine.

Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation of malignant blood cells in the bone marrow. Tumor heterogeneity due to the acquisition of new somatic alterations leads to a high rate of resistance to current therapies or reduces the efficacy of hematopoietic stem cell transplantation (HSCT), thus increasing the risk of relapse and mortality. Single-cell RNA sequencing (scRNA-seq) will enable the classification of AML and guide treatment approaches by profiling patients with different facets of the same disease, stratifying risk, and identifying new potential therapeutic targets at the time of diagnosis or after treatment. ScRNA-seq allows the identification of quiescent stem-like cells, and leukemia stem cells responsible for resistance to therapeutic approaches and relapse after treatment. This method also introduces the factors and mechanisms that enhance the efficacy of the HSCT process. Generated data of the transcriptional profile of the AML could even allow the development of cancer vaccines and CAR T-cell therapies while saving valuable time and alleviating dangerous side effects of chemotherapy and HSCT in vivo. However, scRNA-seq applications face various challenges such as a large amount of data for high-dimensional analysis, technical noise, batch effects, and finding small biological patterns, which could be improved in combination with artificial intelligence models.

Cell signaling communication within papillary craniopharyngioma revealed by an integrated analysis of single-cell RNA sequencing and bulk RNA sequencing

OBJECTIVE

This study aims to elucidate the primary signaling communication among papillary craniopharyngioma (PCP) tumor cells.

METHODS

Five samples of PCP were utilized for single-cell RNA sequencing. The most relevant ligand and receptor interactions among different cells were calculated using the CellChat package in R software. Bulk RNA sequencing of 11 tumor samples and five normal controls was used to investigate the pair interactions detected by single-cell RNA sequencing.

RESULTS

Fibroblasts were not found in ACP, whereas they were detected in PCP. InferCNV revealed high CNV scores for the clusters of epithelial cells and fibroblasts using immune cells as a reference. Epithelial Mesenchymal Transition, Interferon Gamma Response, p53 Pathway, and Estrogen Response Early are pathways commonly shared by fibroblasts and epithelial cells, ranking high in priority. The Wnt signaling pathway and PI3K-Akt signaling pathway play a crucial role in facilitating communication between epithelial cells and fibroblasts. Neutrophils were recognized as the main receivers of incoming signals, with ANXA1-FPR1 and MIF-(CD74 + CXCR2) being identified as the primary signals transmitted from fibroblasts to neutrophils.

CONCLUSION

Through analyzing the communication of essential signaling pathways, ligands, and receptors among epithelial cells, fibroblasts, and neutrophils in PCP tumor tissues, we have identified certain molecules with promising prognostic and therapeutic potential.

Reusable EWOD-based microfluidic system for active droplet generation

Droplets are essential in a wide range of microfluidic applications, but traditional passive droplet generation methods suffer from slow response speed and the need for precise flow rate adjustment. Here, we present an active droplet generation method through electrowetting-on-dielectric (EWOD). Electrowetting is a technique that uses an electric field to change the wettability of a surface. In our method, we apply an electric field to the laminar flow of the dispersed and continuous phases in a microchannel, which induces the discretization of the dispersed thread and leads to droplet formation. A key feature of the proposed active droplet-generating microfluidic device is the reusability of the EWOD actuation substrate, dramatically reducing operational costs. In addition, this approach offers significant advantages over passive methods, including fast response speeds, a wider range of droplet sizes, and greater control over droplet size. In addition, the ultrathin polymer film used in this device allows for a low electrowetting voltage, which helps to prevent damage to encapsulated cells. We believe that our active droplet generation method is a promising new method for generating droplets in microfluidic applications. It is faster, more versatile, and more precise than passive methods, making it ideal for a wide range of applications, including single-cell genomics and drug discovery.

inDrops-2: a flexible, versatile and cost-efficient droplet microfluidic approach for high-throughput scRNA-seq of fresh and preserved clinical samples

The expansion of single-cell analytical techniques has empowered the exploration of diverse biological questions at the individual cells. Droplet-based single-cell RNA sequencing (scRNA-seq) methods have been particularly widely used due to their high-throughput capabilities and small reaction volumes. While commercial systems have contributed to the widespread adoption of droplet-based scRNA-seq, their relatively high cost limits the ability to profile large numbers of cells and samples. Moreover, as the scale of single-cell sequencing continues to expand, accommodating diverse workflows and cost-effective multi-biospecimen profiling becomes more critical. Herein, we present inDrops-2, an open-source scRNA-seq technology designed to profile live or preserved cells with a sensitivity matching that of state-of-the-art commercial systems but at a 6-fold lower cost. We demonstrate the flexibility of inDrops-2, by implementing two prominent scRNA-seq protocols, based on exponential and linear amplification of barcoded-complementary DNA, and provide useful insights into the advantages and disadvantages inherent to each approach. We applied inDrops-2 to simultaneously profile multiple human lung carcinoma samples that had been subjected to cell preservation, long-term storage and multiplexing to obtain a multiregional cellular profile of the tumor microenvironment. The scalability, sensitivity and cost efficiency make inDrops-2 stand out among other droplet-based scRNA-seq methods, ideal for large-scale studies on rare cell molecular signatures.

Optimized methods for scRNA-seq and snRNA-seq of skeletal muscle stored in nucleic acid stabilizing preservative

Single cell studies have transformed our understanding of cellular heterogeneity in disease but the need for fresh starting material can be an obstacle, especially in the context of international multicenter studies and archived tissue. We developed a protocol to obtain high-quality cells and nuclei from dissected human skeletal muscle archived in the preservative Allprotect® Tissue Reagent. After fluorescent imaging microscopy confirmed intact nuclei, we performed four protocol variations that compared sequencing metrics between cells and nuclei enriched by either filtering or flow cytometry sorting. Cells and nuclei (either sorted or filtered) produced statistically identical transcriptional profiles and recapitulated 8 cell types present in skeletal muscle. Flow cytometry sorting successfully enriched for higher-quality cells and nuclei but resulted in an overall decrease in input material. Our protocol provides an important resource for obtaining high-quality single cell genomic material from archived tissue and to streamline global collaborative efforts.

Drug-Induced Differential Gene Expression Analysis on Nanoliter Droplet Microarrays: Enabling Tool for Functional Precision Oncology

Drug-induced differential gene expression analysis (DGEA) is essential for uncovering the molecular basis of cell phenotypic changes and understanding individual tumor responses to anticancer drugs. Performing high throughput DGEA is challenging due to the high cost and labor-intensive multi-step sample preparation protocols. In particular, performing drug-induced DGEA on cancer cells derived from patient biopsies is even more challenging due to the scarcity of available cells. A novel, miniaturized, nanoliter-scale method for drug-induced DGEA is introduced, enabling high-throughput and parallel analysis of patient-derived cell drug responses, overcoming the limitations and laborious nature of traditional protocols. The method is based on the Droplet Microarray (DMA), a microscope glass slide with hydrophilic spots on a superhydrophobic background, facilitating droplet formation for cell testing. DMA allows microscopy-based phenotypic analysis, cDNA extraction, and DGEA. The procedure includes cell lysis for mRNA isolation and cDNA conversion followed by droplet pooling for qPCR analysis. In this study, the drug-induced DGEA protocol on the DMA platform is demonstrated using patient-derived chronic lymphocytic leukemia (CLL) cells. This methodology is critical for DGEA with limited cell numbers and promise for applications in functional precision oncology. This method enables molecular profiling of patient-derived samples after drug treatment, crucial for understanding individual tumor responses to anticancer drugs.

High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution

We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000 + cells per sample. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC offers a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has a multiplet rate of less than 1.1% and a cell capture rate of ~ 50%. Although the assay can accurately profile the whole transcriptome (19,683 human, 21,400 mouse and 21,119 rat genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest - thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of more than 90,000 cells across multiple species and sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate more than 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies with high data quality.

Low expression of CCKBR in the acinar cells is associated with insufficient starch hydrolysis in ruminants

Unlike monogastric animals, ruminants exhibit significantly lower starch digestibility in the small intestine. A better understanding of the physiological mechanisms that regulate digestion patterns in ruminants could lead to an increased use of starch concentrates. Here we show more robust pancreatic exocrine function in adult goats (AG) than in neonatal goats (NG) by combining scRNA-seq and proteomic analysis. Our findings suggest that inadequate amylase activity could be a limiting factor in starch digestion in ruminants. In addition, we show that insufficient starch hydrolysis in adult goats might be associated with low expression of a CCKBR receptor in the acinar cells. On top of that, the low expression of CCKBR in adult goats, in conjunction with a low distribution of the CCK-I cells in the duodenum, may jointly lead to a slow response of the intestinal-pancreatic reflex and induce an asynchronous process of food entering the small intestine and releasing of digestive enzymes, which ultimately limits the starch digestibility. Overall, the present findings generate a resource that can provide better insight into the mammalian pancreas.

Innovative Insights into Single-Cell Technologies and Multi-Omics Integration in Livestock and Poultry

In recent years, single-cell RNA sequencing (scRNA-seq) has marked significant strides in livestock and poultry research, especially when integrated with multi-omics approaches. These advancements provide a nuanced view into complex regulatory networks and cellular dynamics. This review outlines the application of scRNA-seq in key species, including poultry, swine, and ruminants, with a focus on outcomes related to cellular heterogeneity, developmental biology, and reproductive mechanisms. We emphasize the synergistic power of combining scRNA-seq with epigenomic, proteomic, and spatial transcriptomic data, enhancing molecular breeding precision, optimizing health management strategies, and refining production traits in livestock and poultry. The integration of these technologies offers a multidimensional approach that not only broadens the scope of data analysis but also provides actionable insights for improving animal health and productivity.

Droplet-based single-cell sequencing: Strategies and applications

Notable advancements in single-cell omics technologies have not only addressed longstanding challenges but also enabled unprecedented studies of cellular heterogeneity with unprecedented resolution and scale. These strides have led to groundbreaking insights into complex biological systems, paving the way for a more profound comprehension of human biology and diseases. The droplet microfluidic technology has become a crucial component in many single-cell sequencing workflows in terms of throughput, cost-effectiveness, and automation. Utilizing a microfluidic chip to encapsulate and profile individual cells within droplets has significantly improved single-cell research. Therefore, this review aims to comprehensively elaborate the droplet microfluidics-assisted omics methods from a single-cell perspective. The strategies for using droplet microfluidics in the realms of genomics, epigenomics, transcriptomics, and proteomics analyses are first introduced. On this basis, the focus then turns to the latest applications of this technology in different sequencing patterns, including mono- and multi-omics. Finally, the challenges and further perspectives of droplet-based single-cell sequencing in both foundational research and commercial applications are discussed.

Transcriptome and Temporal Transcriptome Analyses in Single Cells

Transcriptome analysis in single cells, enabled by single-cell RNA sequencing, has become a prevalent approach in biomedical research, ranging from investigations of gene regulation to the characterization of tissue organization. Over the past decade, advances in single-cell RNA sequencing technology, including its underlying chemistry, have significantly enhanced its performance, marking notable improvements in methodology. A recent development in the field, which integrates RNA metabolic labeling with single-cell RNA sequencing, has enabled the profiling of temporal transcriptomes in individual cells, offering new insights into dynamic biological processes involving RNA kinetics and cell fate determination. In this review, we explore the chemical principles and design improvements that have enhanced single-molecule capture efficiency, improved RNA quantification accuracy, and increased cellular throughput in single-cell transcriptome analysis. We also illustrate the concept of RNA metabolic labeling for detecting newly synthesized transcripts and summarize recent advancements that enable single-cell temporal transcriptome analysis. Additionally, we examine data analysis strategies for the precise quantification of newly synthesized transcripts and highlight key applications of transcriptome and temporal transcriptome analyses in single cells.

Needle-Plug/Piston-Based Modular Mesoscopic Design Paradigm Coupled With Microfluidic Device for Point-of-Care Pooled Testing

Emerging diagnostic scenarios, such as population surveillance by pooled testing and on-site rapid diagnosis, highlight the importance of advanced microfluidic systems for in vitro diagnostics. However, the widespread adoption of microfluidic technology faces challenges due to the lack of standardized design paradigms, posing difficulties in managing macro-micro fluidic interfaces, reagent storage, and complex macrofluidic operations. This paper introduces a novel modular-based mesoscopic design paradigm, featuring a core "needle-plug/piston" structure with versatile variants for complex fluidic operations. These structures can be easily coupled with various microfluidic platforms to achieve truly self-contained microsystems. Incorporated into a "3D extensible" design architecture, the mesoscopic design meets the demands of function integration, macrofluid manipulations, and flexible throughputs for point-of-care nucleic acid testing. Using this approach, an ultra-sensitive nucleic acid detection system is developed with a limit of detection of ten copies of SARS-CoV-2 per mL. This system efficiently conducts large-scale pooled testing from 50 pharyngeal swabs in a tube with an uncompromised sensitivity, enabling a truly "sample-in-answer-out" microsystem with exceptional performance.

Single-cell spatial transcriptomics in cardiovascular development, disease, and medicine

Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., "angina-myocardial infarction-heart failure" in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.

Detecting significant expression patterns in single-cell and spatial transcriptomics with a flexible computational approach

Gene expression data holds the potential to shed light on multiple biological processes at once. However, data analysis methods for single cell sequencing mostly focus on finding cell clusters or the principal progression line of the data. Data analysis for spatial transcriptomics mostly addresses clustering and finding spatially variable genes. Existing data analysis methods are effective in finding the main data features, but they might miss less pronounced, albeit significant, processes, possibly involving a subset of the samples. In this work we present SPIRAL: Significant Process InfeRence ALgorithm. SPIRAL is based on Gaussian statistics to detect all statistically significant biological processes in single cell, bulk and spatial transcriptomics data. The algorithm outputs a list of structures, each defined by a set of genes working simultaneously in a specific population of cells. SPIRAL is unique in its flexibility: the structures are constructed by selecting subsets of genes and cells based on statistically significant and consistent differential expression. Every gene and every cell may be part of one structure, more or none. SPIRAL also provides several visual representations of structures and pathway enrichment information. We validated the statistical soundness of SPIRAL on synthetic datasets and applied it to single cell, spatial and bulk RNA-sequencing datasets. SPIRAL is available at https://spiral.technion.ac.il/ .

Sex-specific single cell-level transcriptomic signatures of Rett syndrome disease progression

Dominant X-linked diseases are uncommon due to female X chromosome inactivation (XCI). While random XCI usually protects females against X-linked mutations, Rett syndrome (RTT) is a female neurodevelopmental disorder caused by heterozygous MECP2 mutation. After 6-18 months of typical neurodevelopment, RTT girls undergo a poorly understood regression. We performed longitudinal snRNA-seq on cerebral cortex in a construct-relevant Mecp2e1 mutant mouse model of RTT, revealing transcriptional effects of cell type, mosaicism, and sex on progressive disease phenotypes. Across cell types, we observed sex differences in the number of differentially expressed genes (DEGs) with 6x more DEGs in mutant females than males. Unlike males, female DEGs emerged prior to symptoms, were enriched for homeostatic gene pathways in distinct cell types over time and correlated with disease phenotypes and human RTT cortical cell transcriptomes. Non-cell-autonomous effects were prominent and dynamic across disease progression of Mecp2e1 mutant females, indicating that wild-type-expressing cells normalize transcriptional homeostasis. These results advance our understanding of RTT progression and treatment.

Single-cell profiling of cellular changes in the somatic peripheral nerves following nerve injury

Injury to the peripheral nervous system disconnects targets to the central nervous system, disrupts signal transmission, and results in functional disability. Although surgical and therapeutic treatments improve nerve regeneration, it is generally hard to achieve fully functional recovery after severe peripheral nerve injury. A better understanding of pathological changes after peripheral nerve injury helps the development of promising treatments for nerve regeneration. Single-cell analyses of the peripheral nervous system under physiological and injury conditions define the diversity of cells in peripheral nerves and reveal cell-specific injury responses. Herein, we review recent findings on the single-cell transcriptome status in the dorsal root ganglia and peripheral nerves following peripheral nerve injury, identify the cell heterogeneity of peripheral nerves, and delineate changes in injured peripheral nerves, especially molecular changes in neurons, glial cells, and immune cells. Cell-cell interactions in peripheral nerves are also characterized based on ligand-receptor pairs from coordinated gene expressions. The understanding of cellular changes following peripheral nerve injury at a single-cell resolution offers a comprehensive and insightful view for the peripheral nerve repair process, provides an important basis for the exploration of the key regulators of neuronal growth and microenvironment reconstruction, and benefits the development of novel therapeutic drugs for the treatment of peripheral nerve injury.

Single-cell profiling identifies LIN28A mRNA targets in the mouse pluripotent-to-2C-like transition and somatic cell reprogramming

RNA-binding proteins (RBPs) regulate totipotency, pluripotency maintenance, and induction. The intricacies of how they modulate these processes through their interaction with RNAs remain to be elucidated. Here we employed Targets of RBPs Identified By Editing (TRIBE) with single-cell resolution (scTRIBE) to profile the mRNA targets of the key pluripotency regulator LIN28A in mouse embryonic stem cells (ESCs), 2-cell embryo-like cells (2CLCs), and somatic cell reprogramming. LIN28A is known to act by controlling the maturation of the let-7 microRNA, but, in addition, it binds to multiple mRNAs and influences their stability and translation efficiency. However, the mRNA targets of LIN28A in 2CLCs and reprogramming are unclear. Through quantitative single-cell analysis of the scTRIBE dataset, we observed a marked increase in the binding of LIN28A to mRNAs of ribosome biogenesis factors and a selected group of totipotency factors in 2CLCs within ESC cultures. Our results suggest that LIN28A extends the half-life of at least some of these mRNAs, providing new insights into its role in the totipotent state. We also uncovered the distinct trajectory-specific LIN28A-mRNA networks in reprogramming, helping explain how LIN28A facilitates the mesenchymal-to-epithelial transition and pluripotency acquisition. Our study not only clarifies the multifunctional role of LIN28A in these processes but also highlights the importance of decoding RNA-protein interactions at the single-cell level.

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

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

Robust Double Emulsions for Multicolor Fluorescence-Activated Cell Sorting

Cell-cell interactions are essential for the proper functioning of multicellular organisms. For example, T cells interact with antigen-presenting cells (APCs) through specific T-cell receptor (TCR)-antigen interactions during an immune response. Fluorescence-activated droplet sorting (FADS) is a high-throughput technique for efficiently screening cellular interaction events. Unfortunately, current droplet sorting instruments have significant limitations, most notably related to analytical throughput and complex operation. In contrast, commercial fluorescence-activated cell sorters offer superior speed, sensitivity, and multiplexing capabilities, although their use as droplet sorters is poorly defined and underutilized. Herein, we present a universally applicable and simple-to-implement workflow for generating double emulsions and performing multicolor cell sorting using a commercial FACS instrument. This workflow achieves a double emulsion detection rate exceeding 90%, enabling multicellular encapsulation and high-throughput immune cell activation sorting for the first time. We anticipate that the presented droplet sorting strategy will benefit cell biology laboratories by providing access to an advanced microfluidic toolbox with minimal effort and cost investment.

Performance comparison of high throughput single-cell RNA-Seq platforms in complex tissues

Single-cell transcriptomics has emerged as the preferred tool to define cell identity through the analysis of gene expression signatures. However, there are limited studies that have comprehensively compared the performance of different scRNAseq systems in complex tissues. Here, we present a systematic comparison of two well-established high throughput 3'-scRNAseq platforms: 10× Chromium and BD Rhapsody, using tumours that present high cell diversity. Our experimental design includes both fresh and artificially damaged samples from the same tumours, which also provides a comparable dataset to examine their performance under challenging conditions. The performance metrics used in this study consist of gene sensitivity, mitochondrial content, reproducibility, clustering capabilities, cell type representation and ambient RNA contamination. These analyses showed that BD Rhapsody and 10× Chromium have similar gene sensitivity, while BD Rhapsody has the highest mitochondrial content. Interestingly, we found cell type detection biases between platforms, including a lower proportion of endothelial and myofibroblast cells in BD Rhapsody and lower gene sensitivity in granulocytes for 10× Chromium. Moreover, the source of the ambient noise was different between plate-based and droplet-based platforms. In conclusion, our reported platform differential performance should be considered for the selection of the scRNAseq method during the study experimental designs.

Single-cell transcriptomics reveals heterogeneity in plant responses to the environment: a focus on biotic and abiotic interactions

Biotic and abiotic environmental cues are major factors influencing plant growth and productivity. Interactions with biotic (e.g. symbionts and pathogens) and abiotic (e.g. changes in temperature, water, or nutrient availability) factors trigger signaling and downstream transcriptome adjustments in plants. While bulk RNA-sequencing technologies have traditionally been used to profile these transcriptional changes, tissue homogenization may mask heterogeneity of responses resulting from the cellular complexity of organs. Thus, whether different cell types respond equally to environmental fluctuations, or whether subsets of the responses are cell-type specific, are long-lasting questions in plant biology. The recent breakthrough of single-cell transcriptomics in plant research offers an unprecedented view of cellular responses under changing environmental conditions. In this review, we discuss the contribution of single-cell transcriptomics to the understanding of cell-type-specific plant responses to biotic and abiotic environmental interactions. Besides major biological findings, we present some technical challenges coupled to single-cell studies of plant-environment interactions, proposing possible solutions and exciting paths for future research.

Mouse testicular macrophages can independently produce testosterone and are regulated by Cebpb

BACKGROUND

Testicular macrophages (TM) have long been recognized for their role in immune response within the testicular environment. However, their involvement in steroid hormone synthesis, particularly testosterone, has not been fully elucidated. This study aims to explore the capability of TM to synthesize and secrete testosterone de novo and to investigate the regulatory mechanisms involved.

RESULTS

Transcriptomic analysis revealed significant expression of Cyp11a1, Cyp17a1, Hsd3b1, and Hsd17b3 in TM, which are key enzymes in the testosterone synthesis pathway. qPCR analysis and immunofluorescence validation confirmed the autonomous capability of TM to synthesize testosterone. Ablation of TM in mice resulted in decreased physiological testosterone levels, underscoring the significance of TM in maintaining testicular testosterone levels. Additionally, the study also demonstrated that Cebpb regulates the expression of these crucial genes, thereby modulating testosterone synthesis.

CONCLUSIONS

This research establishes that TM possess the autonomous capacity to synthesize and secrete testosterone, contributing significantly to testicular testosterone levels. The transcription factor Cebpb plays a crucial role in this process by regulating the expression of key genes involved in testosterone synthesis.

Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives

Spermatogenesis, a key part of the spermiation process, is regulated by a combination of key cells, such as primordial germ cells, spermatogonial stem cells, and somatic cells, such as Sertoli cells. Abnormal spermatogenesis can lead to azoospermia, testicular tumors, and other diseases related to male infertility. The application of single-cell RNA sequencing (scRNA-seq) technology in male reproduction is gradually increasing with its unique insight into deep mining and analysis. The data cover different periods of neonatal, prepubertal, pubertal, and adult stages. Different types of male infertility diseases including obstructive and non-obstructive azoospermia (NOA), Klinefelter Syndrome (KS), Sertoli Cell Only Syndrome (SCOS), and testicular tumors are also covered. We briefly review the principles and application of scRNA-seq and summarize the research results and application directions in spermatogenesis in different periods and pathological states. Moreover, we discuss the challenges of applying this technology in male reproduction and the prospects of combining it with other technologies.

Single-cell RNA sequencing in ovarian cancer: revealing new perspectives in the tumor microenvironment

Single-cell sequencing technology has emerged as a pivotal tool for unraveling the complexities of the ovarian tumor microenvironment (TME), which is characterized by its cellular heterogeneity and intricate cell-to-cell interactions. Ovarian cancer (OC), known for its high lethality among gynecologic malignancies, presents significant challenges in treatment and diagnosis, partly due to the complexity of its TME. The application of single-cell sequencing in ovarian cancer research has enabled the detailed characterization of gene expression profiles at the single-cell level, shedding light on the diverse cell populations within the TME, including cancer cells, stromal cells, and immune cells. This high-resolution mapping has been instrumental in understanding the roles of these cells in tumor progression, invasion, metastasis, and drug resistance. By providing insight into the signaling pathways and cell-to-cell communication mechanisms, single-cell sequencing facilitates the identification of novel therapeutic targets and the development of personalized medicine approaches. This review summarizes the advancement and application of single-cell sequencing in studying the stromal components and the broader TME in OC, highlighting its implications for improving diagnosis, treatment strategies, and understanding of the disease's underlying biology.

Recent advances in drug delivery applications of aqueous two-phase systems

Aqueous two-phase systems (ATPSs) are liquid-liquid equilibria between two aqueous phases that usually contain over 70% water content each, which results in a nontoxic organic solvent-free environment for biological compounds and biomolecules. ATPSs have attracted significant interest in applications for formulating carriers (microparticles, nanoparticles, hydrogels, and polymersomes) which can be prepared using the spontaneous phase separation of ATPSs as a driving force, and loaded with a wide range of bioactive materials, including small molecule drugs, proteins, and cells, for delivery applications. This review provides a detailed analysis of various ATPSs, including strategies employed for particle formation, polymerization of droplets in ATPSs, phase-guided block copolymer assemblies, and stimulus-responsive carriers. Processes for loading various bioactive payloads are discussed, and applications of these systems for drug delivery are summarized and discussed.

Advance and Application of Single-cell Transcriptomics in Auditory Research

Hearing loss and deafness, as a worldwide disability disease, have been troubling human beings. However, the auditory organ of the inner ear is highly heterogeneous and has a very limited number of cells, which are largely uncharacterized in depth. Recently, with the development and utilization of single-cell RNA sequencing (scRNA-seq), researchers have been able to unveil the complex and sophisticated biological mechanisms of various types of cells in the auditory organ at the single-cell level and address the challenges of cellular heterogeneity that are not resolved through by conventional bulk RNA sequencing (bulk RNA-seq). Herein, we reviewed the application of scRNA-seq technology in auditory research, with the aim of providing a reference for the development of auditory organs, the pathogenesis of hearing loss, and regenerative therapy. Prospects about spatial transcriptomic scRNA-seq, single-cell based genome, and Live-seq technology will also be discussed.

Advances in single-cell sequencing technology in microbiome research

With the rapid development of histological techniques and the widespread application of single-cell sequencing in eukaryotes, researchers desire to explore individual microbial genotypes and functional expression, which deepens our understanding of microorganisms. In this review, the history of the development of microbial detection technologies was revealed and the difficulties in the application of single-cell sequencing in microorganisms were dissected as well. Moreover, the characteristics of the currently emerging microbial single-cell sequencing (Microbe-seq) technology were summarized, and the prospects of the application of Microbe-seq in microorganisms were distilled based on the current development status. Despite its mature development, the Microbe-seq technology was still in the optimization stage. A retrospective study was conducted, aiming to promote the widespread application of single-cell sequencing in microorganisms and facilitate further improvement in the technology.

Revealing gene expression heterogeneity in a clonal population of Tetrahymena thermophila through single-cell RNA sequencing

We performed single-cell RNA sequencing (scRNA-seq) on a population of 5,000 Tetrahymena thermophila, using the 10x Genomics 3' gene expression analysis, to investigate gene expression variability within this clonal population. Initially, we estimated the 3'-untranslated regions (3' UTRs), which were absent in existing annotation files but are crucial for the 10x Genomics 3' gene expression analysis, using the peaks2utr method. This allowed us to create a modified annotation file, which was then utilized in our scRNA-seq analysis. Our analysis revealed significant gene expression variability within the population, even after removing the effect of cell phase-related features. This variability predominantly appeared in six distinct clusters. Through gene ontology and KEGG pathway enrichment analyses, we identified that these were primarily associated with ribosomal proteins, proteins specific to mitochondria, proteins involved in peroxisome-specific carbon metabolism, cytoskeletal proteins, motor proteins, and immobilized antigens.

A detection system using sensing motif-tethered oligodeoxynucleotides for multiplex biomolecular analysis

We developed a system to detect multiple target biomolecules through sensing motif-tethered oligodeoxynucleotides. DNA-based molecular probes gave the primary amine motif upon reaction with the target biomolecules, glutathione (GSH) and H2O2. After labelling with biotin, the product DNAs were selectively collected to be quantified by qPCR.

Status of single-cell RNA sequencing for reproductive toxicology in zebrafish and the transcriptomic trade-off

The utilization of transcriptomic studies identifying profiles of gene expression, especially in toxicogenomics, has catapulted next-generation sequencing to the forefront of reproductive toxicology. An innovative yet underutilized RNA sequencing technique emerging into this field is single-cell RNA sequencing (scRNA-seq), which provides sequencing at the individual cellular level of gonad tissue. ScRNA-seq provides a novel and unique perspective for identifying distinct cellular profiles, including identification of rare cell subtypes. The specificity of scRNA-seq is a powerful tool for reproductive toxicity research, especially for translational animal models including zebrafish. Studies to date not only have focused on 'tissue atlassing' or characterizing what cell types make up different tissues but have also begun to include toxicant exposure as a factor that this review aims to explore. Future scRNA-seq studies will contribute to understanding exposure-induced outcomes; however, the trade-offs with traditional methods need to be considered.

Single-Cell Sequencing: High-Resolution Analysis of Cellular Heterogeneity in Autoimmune Diseases

Autoimmune diseases (AIDs) are complex in etiology and diverse in classification but clinically show similar symptoms such as joint pain and skin problems. As a result, the diagnosis is challenging, and usually, only broad treatments can be available. Consequently, the clinical responses in patients with different types of AIDs are unsatisfactory. Therefore, it is necessary to conduct more research to figure out the pathogenesis and therapeutic targets of AIDs. This requires research technologies with strong extraction and prediction capabilities. Single-cell sequencing technology analyses the genomic, epigenomic, or transcriptomic information at the single-cell level. It can define different cell types and states in greater detail, further revealing the molecular mechanisms that drive disease progression. These advantages enable cell biology research to achieve an unprecedented resolution and scale, bringing a whole new vision to life science research. In recent years, single-cell technology especially single-cell RNA sequencing (scRNA-seq) has been widely used in various disease research. In this paper, we present the innovations and applications of single-cell sequencing in the medical field and focus on the application contributing to the differential diagnosis and precise treatment of AIDs. Despite some limitations, single-cell sequencing has a wide range of applications in AIDs. We finally present a prospect for the development of single-cell sequencing. These ideas may provide some inspiration for subsequent research.

Advancing skeletal health and disease research with single-cell RNA sequencing

Orthopedic conditions have emerged as global health concerns, impacting approximately 1.7 billion individuals worldwide. However, the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders. The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity. Nevertheless, investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges. In this comprehensive review, we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines. By utilizing these methodologies, crucial insights into the developmental dynamics, maintenance of homeostasis, and pathological processes involved in spine, joint, bone, muscle, and tendon disorders have been uncovered. Specifically focusing on the joint diseases of degenerative disc disease, osteoarthritis, and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension. These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.

Sex-specific single cell-level transcriptomic signatures of Rett syndrome disease progression

Dominant X-linked diseases are uncommon due to female X chromosome inactivation (XCI). While random XCI usually protects females against X-linked mutations, Rett syndrome (RTT) is a female neurodevelopmental disorder caused by heterozygous MECP2 mutation. After 6-18 months of typical neurodevelopment, RTT girls undergo poorly understood regression. We performed longitudinal snRNA-seq on cerebral cortex in a construct-relevant Mecp2e1 mutant mouse model of RTT, revealing transcriptional effects of cell type, mosaicism, and sex on progressive disease phenotypes. Across cell types, we observed sex differences in the number of differentially expressed genes (DEGs) with 6x more DEGs in mutant females than males. Unlike males, female DEGs emerged prior to symptoms, were enriched for homeostatic gene pathways in distinct cell types over time, and correlated with disease phenotypes and human RTT cortical cell transcriptomes. Non-cell-autonomous effects were prominent and dynamic across disease progression of Mecp2e1 mutant females, indicating wild-type-expressing cells normalizing transcriptional homeostasis. These results improve understanding of RTT progression and treatment.

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.

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

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

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets

BACKGROUND

Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data.

MAIN BODY

The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis.

CONCLUSIONS

According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

Single-cell transcriptomics: background, technologies, applications, and challenges

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

Comparative analysis of 10X Chromium vs. BD Rhapsody whole transcriptome single-cell sequencing technologies in complex human tissues

The development of single-cell omics tools has enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, each of the different techniques may have its unique strengths and limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium vs. microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy. Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of cell populations differed. Cells with low mRNA content such as T cells were underrepresented in the droplet-based system, at least partly due to lower RNA capture rates. In contrast, microwell-based scRNA-seq recovered less cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation. Overall, our study provides important information for selection of the appropriate scRNA-seq platform and for the interpretation of published results.

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.

Expression characteristics and potential function of non-coding RNA in mouse cortical cells

Non-coding RNAs (ncRNAs) play essential regulatory functions in various physiological and pathological processes in the brain. To systematically characterize the ncRNA profile in cortical cells, we downloaded single-cell SMART-Seq v4 data of mouse cerebral cortex. Our results revealed that the ncRNAs alone are sufficient to define the identity of most cortical cell types. We identified 1,600 ncRNAs that exhibited cell type specificity, even yielding to distinguish microglia from perivascular macrophages with ncRNA. Moreover, we characterized cortical layer and region specific ncRNAs, in line with the results by spatial transcriptome (ST) data. By constructing a co-expression network of ncRNAs and protein-coding genes, we predicted the function of ncRNAs. By integrating with genome-wide association studies data, we established associations between cell type-specific ncRNAs and traits related to neurological disorders. Collectively, our study identified differentially expressed ncRNAs at multiple levels and provided the valuable resource to explore the functions and dysfunctions of ncRNAs in cortical cells.

Metacell-based differential expression analysis identifies cell type specific temporal gene response programs in COVID-19 patient PBMCs

By profiling gene expression in individual cells, single-cell RNA-sequencing (scRNA-seq) can resolve cellular heterogeneity and cell-type gene expression dynamics. Its application to time-series samples can identify temporal gene programs active in different cell types, for example, immune cells' responses to viral infection. However, current scRNA-seq analysis has limitations. One is the low number of genes detected per cell. The second is insufficient replicates (often 1-2) due to high experimental cost. The third lies in the data analysis-treating individual cells as independent measurements leads to inflated statistics. To address these, we explore a new computational framework, specifically whether "metacells" constructed to maintain cellular heterogeneity within individual cell types (or clusters) can be used as "replicates" for increasing statistical rigor. Toward this, we applied SEACells to a time-series scRNA-seq dataset from peripheral blood mononuclear cells (PBMCs) after SARS-CoV-2 infection to construct metacells, and used them in maSigPro for quadratic regression to find significantly differentially expressed genes (DEGs) over time, followed by clustering expression velocity trends. We showed that such metacells retained greater expression variances and produced more biologically meaningful DEGs compared to either metacells generated randomly or from simple pseudobulk methods. More specifically, this approach correctly identified the known ISG15 interferon response program in almost all PBMC cell types and many DEGs enriched in the previously defined SARS-CoV-2 infection response pathway. It also uncovered additional and more cell type-specific temporal gene expression programs. Overall, our results demonstrate that the metacell-pseudoreplicate strategy could potentially overcome the limitation of 1-2 replicates.

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.