Single-cell barcoding and sequencing using droplet microfluidics

SCInter: A comprehensive single-cell transcriptome integration database for human and mouse

Single-cell RNA sequencing (scRNA-seq), which profiles gene expression at the cellular level, has effectively explored cell heterogeneity and reconstructed developmental trajectories. With the increasing research on diseases and biological processes, scRNA-seq datasets are accumulating rapidly, highlighting the urgent need for collecting and processing these data to support comprehensive and effective annotation and analysis. Here, we have developed a comprehensive Single-Cell transcriptome integration database for human and mouse (SCInter, https://bio.liclab.net/SCInter/index.php), which aims to provide a manually curated database that supports the provision of gene expression profiles across various cell types at the sample level. The current version of SCInter includes 115 integrated datasets and 1016 samples, covering nearly 150 tissues/cell lines. It contains 8016,646 cell markers in 457 identified cell types. SCInter enabled comprehensive analysis of cataloged single-cell data encompassing quality control (QC), clustering, cell markers, multi-method cell type automatic annotation, predicting cell differentiation trajectories and so on. At the same time, SCInter provided a user-friendly interface to query, browse, analyze and visualize each integrated dataset and single cell sample, along with comprehensive QC reports and processing results. It will facilitate the identification of cell type in different cell subpopulations and explore developmental trajectories, enhancing the study of cell heterogeneity in the fields of immunology and oncology.

Drop-by-Drop Addition of Reagents to a Double Emulsion

Developments in droplet microfluidics have facilitated an era of high-throughput, sensitive single-cell, or single-molecule measurements capable of tackling the heterogeneity present in biological systems. Relying on single emulsion (SE) compartments, droplet assays achieve absolute quantification of nucleic acids, massively parallel single-cell profiling, and more. Double emulsions (DEs) have seen recent interest for their potential to build upon SE techniques. DEs are compatible with flow cytometry enabling high-throughput multi-parameter drop screening and eliminate content mixing due to coalescence during lengthy workflows. Despite these strengths, DEs lack important technical functions that exist in SEs such as methods for adding reagents to droplets on demand. Consequently, DEs cannot be used for multistep workflows which has limited their adoption in assay development. Here, strategies to enable reagent addition and other active manipulations on DEs are reported by converting DE inputs to SEs on chip. After conversion, drops are manipulated using existing SE techniques, including reagent addition, before reforming a DE at the outlet. Device designs and operation conditions achieving drop-by-drop reagent addition to DEs are identified and used as part of a multi-step aptamer screening assay performed entirely in DE drops. This work enables the further development of multistep DE droplet assays.

Magnetic Stirring Device for Limiting the Sedimentation of Cells inside Microfluidic Devices

In experiments considering cell handling in microchannels, cell sedimentation in the storage container is a key problem because it affects the reproducibility of the experiments. Here, a simple and low-cost cell mixing device (CMD) is presented; the device is designed to prevent the sedimentation of cells in a syringe during their injection into a microfluidic channel. The CMD is based on a slider crank device made of 3D-printed parts that, combined with a permanent magnet, actuate a stir bar placed into the syringe containing the cells. By using A549 cell lines, the device is characterized in terms of cell viability (higher than 95%) in different mixing conditions, by varying the oscillation frequency and the overall mixing time. Then, a dedicated microfluidic experiment is designed to evaluate the injection frequency of the cells within a microfluidic chip. In the presence of the CMD, a higher number of cells are injected into the microfluidic chip with respect to the static conditions (2.5 times), proving that it contrasts cell sedimentation and allows accurate cell handling. For these reasons, the CMD can be useful in microfluidic experiments involving single-cell analysis.

Data acquisition approaches for single cell proteomics

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.

Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods

Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.

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

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

Single Nucleus Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Gliomas

Gliomas, the predominant form of brain cancer, comprise diverse malignant subtypes with limited curative therapies available. The insufficient understanding of their molecular diversity and evolutionary processes hinders the advancement of new treatments. Technical complexities associated with formalin-fixed paraffin-embedded (FFPE) clinical samples hinder molecular-level analyses of gliomas. Current single-cell RNA sequencing (scRNA-seq) platforms are inadequate for large-scale clinical applications. In this study, automated snRandom-seq is developed, a high-throughput single-nucleus total RNA sequencing platform optimized for archival FFPE samples. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, accommodating a broad spectrum of sample types. The automated snRandom-seq is applied to analyze 116 492 single nuclei from 17 FFPE samples of various glioma subtypes, including rare clinical samples and matched primary-recurrent glioblastomas (GBMs). The study provides comprehensive insights into the molecular characteristics of gliomas at the single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles across different glioma clusters and uncovered promising recurrence-related targets and pathways in primary-recurrent GBMs are identified. These findings establish automated snRandom-seq as a robust tool for scRNA-seq of FFPE samples, enabling exploration of molecular diversities and tumor evolution. This platform holds significant implications for large-scale integrative and retrospective clinical research.

Building and analyzing metacells in single-cell genomics data

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

Fabrication of Crescent Shaped Microparticles for Particle Templated Droplet Formation

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

Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotype

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of renal cancer, accounting for over 75% of cases. The asymptomatic nature of the disease contributes to late-stage diagnoses and poor survival. Highly vascularized and immune infiltrated microenvironment are prominent features of ccRCC, yet the interplay between vasculature and immune cells, disease progression and response to therapy remains poorly understood. Using droplet-based single-cell RNA sequencing we profile 50,236 transcriptomes from paired tumor and healthy adjacent kidney tissues. Our analysis reveals significant heterogeneity and inter-patient variability of the tumor microenvironment. Notably, we discover a previously uncharacterized vasculature subpopulation associated with epithelial-mesenchymal transition. The cell-cell communication analysis reveals multiple modes of immunosuppressive interactions within the tumor microenvironment, including clinically relevant interactions between tumor vasculature and stromal cells with immune cells. The upregulation of the genes involved in these interactions is associated with worse survival in the TCGA KIRC cohort. Our findings demonstrate the role of tumor vasculature and stromal cell populations in shaping the ccRCC microenvironment and uncover a subpopulation of cells within the tumor vasculature that is associated with an angiogenic phenotype.

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.

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.

Cellular and molecular characteristics of stromal Lkb1 deficiency-induced gastrointestinal polyposis based on single-cell RNA sequencing

Liver kinase B1 (Lkb1), encoded by serine/threonine kinase (Stk11), is a serine/threonine kinase and tumor suppressor that is strongly implicated in Peutz-Jeghers syndrome (PJS). Numerous studies have shown that mesenchymal-specific Lkb1 is sufficient for the development of PJS-like polyps in mice. However, the cellular origin and components of these Lkb1-associated polyps and underlying mechanisms remain elusive. In this study, we generated tamoxifen-inducible Lkb1flox/flox;Myh11-Cre/ERT2 and Lkb1flox/flox;PDGFRα-Cre/ERT2 mice, performed single-cell RNA sequencing (scRNA-seq) and imaging-based lineage tracing, and aimed to investigate the cellular complexity of gastrointestinal polyps associated with PJS. We found that Lkb1flox/+;Myh11-Cre/ERT2 mice developed gastrointestinal polyps starting at 9 months after tamoxifen treatment. scRNA-seq revealed aberrant stem cell-like characteristics of epithelial cells from polyp tissues of Lkb1flox/+;Myh11-Cre/ERT2 mice. The Lkb1-associated polyps were further characterized by a branching smooth muscle core, abundant extracellular matrix deposition, and high immune cell infiltration. In addition, the Spp1-Cd44 or Spp1-Itga8/Itgb1 axes were identified as important interactions among epithelial, mesenchymal, and immune compartments in Lkb1-associated polyps. These characteristics of gastrointestinal polyps were also demonstrated in another mouse model, tamoxifen-inducible Lkb1flox/flox;PDGFRα-Cre/ERT2 mice, which developed obvious gastrointestinal polyps as early as 2-3 months after tamoxifen treatment. Our findings further confirm the critical role of mesenchymal Lkb1/Stk11 in gastrointestinal polyposis and provide novel insight into the cellular complexity of Lkb1-associated polyp biology. © 2024 The Pathological Society of Great Britain and Ireland.

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

A map of signaling responses in the human airway epithelium

Receptor-mediated signaling plays a central role in tissue regeneration, and it is dysregulated in disease. Here, we build a signaling-response map for a model regenerative human tissue: the airway epithelium. We analyzed the effect of 17 receptor-mediated signaling pathways on organotypic cultures to determine changes in abundance and phenotype of epithelial cell types. This map recapitulates the gamut of known airway epithelial signaling responses to these pathways. It defines convergent states induced by multiple ligands and diverse, ligand-specific responses in basal cell and secretory cell metaplasia. We show that loss of canonical differentiation induced by multiple pathways is associated with cell-cycle arrest, but that arrest is not sufficient to block differentiation. Using the signaling-response map, we show that a TGFB1-mediated response underlies specific aberrant cells found in multiple lung diseases and identify interferon responses in COVID-19 patient samples. Thus, we offer a framework enabling systematic evaluation of tissue signaling responses. A record of this paper's transparent peer review process is included in the supplemental information.

Opto-combinatorial indexing enables high-content transcriptomics by linking cell images and transcriptomes

We introduce a simple integrated analysis method that links cellular phenotypic behaviour with single-cell RNA sequencing (scRNA-seq) by utilizing a combination of optical indices from cells and hydrogel beads. With our method, the combinations, referred to as joint colour codes, enable the link via matching the optical combinations measured by conventional epi-fluorescence microscopy with the concatenated DNA molecular barcodes created by cell-hydrogel bead pairs and sequenced by next-generation sequencing. We validated our approach by demonstrating an accurate link between the cell image and scRNA-seq with mixed species experiments, longitudinal cell tagging by electroporation and lipofection, and gene expression analysis. Furthermore, we extended our approach to multiplexed chemical transcriptomics, which enabled us to identify distinct phenotypic behaviours in HeLa cells treated with various concentrations of paclitaxel, and determine the corresponding gene regulation associated with the formation of a multipolar spindle.

Disruption of Cholinergic Retinal Waves Alters Visual Cortex Development and Function

Retinal waves represent an early form of patterned spontaneous neural activity in the visual system. These waves originate in the retina before eye-opening and propagate throughout the visual system, influencing the assembly and maturation of subcortical visual brain regions. However, because it is technically challenging to ablate retina-derived cortical waves without inducing compensatory activity, the role these waves play in the development of the visual cortex remains unclear. To address this question, we used targeted conditional genetics to disrupt cholinergic retinal waves and their propagation to select regions of primary visual cortex, which largely prevented compensatory patterned activity. We find that loss of cholinergic retinal waves without compensation impaired the molecular and synaptic maturation of excitatory neurons located in the input layers of visual cortex, as well as layer 1 interneurons. These perinatal molecular and synaptic deficits also relate to functional changes observed at later ages. We find that the loss of perinatal cholinergic retinal waves causes abnormal visual cortex retinotopy, mirroring changes in the retinotopic organization of gene expression, and additionally impairs the processing of visual information. We further show that retinal waves are necessary for higher order processing of sensory information by impacting the state-dependent activity of layer 1 interneurons, a neuronal type that shapes neocortical state-modulation, as well as for state-dependent gain modulation of visual responses of excitatory neurons. Together, these results demonstrate that a brief targeted perinatal disruption of patterned spontaneous activity alters early cortical gene expression as well as synaptic and physiological development, and compromises both fundamental and, notably, higher-order functions of visual cortex after eye-opening.

Advancements in Single-Cell RNA Sequencing Research for Neurological Diseases

Neurological diseases are a major cause of the global burden of disease. Although the mechanisms of the occurrence and development of neurological diseases are not fully clear, most of them are associated with cells mediating neuroinflammation. Yet medications and other therapeutic options to improve treatment are still very limited. Single-cell RNA sequencing (scRNA-seq), as a delightfully potent breakthrough technology, not only identifies various cell types and response states but also uncovers cell-specific gene expression changes, gene regulatory networks, intercellular communication, and cellular movement trajectories, among others, in different cell types. In this review, we describe the technology of scRNA-seq in detail and discuss and summarize the application of scRNA-seq in exploring neurological diseases, elaborating the corresponding specific mechanisms of the diseases as well as providing a reliable basis for new therapeutic approaches. Finally, we affirm that scRNA-seq promotes the development of the neuroscience field and enables us to have a deeper cellular understanding of neurological diseases in the future, which provides strong support for the treatment of neurological diseases and the improvement of patients' prognosis.

In vitro models for neuropathic pain phenotypic screening in brain therapeutics

The discovery of brain therapeutics faces a significant challenge due to the low translatability of preclinical results into clinical success. To address this gap, several efforts have been made to obtain more translatable neuronal models for phenotypic screening. These models allow the selection of active compounds without predetermined knowledge of drug targets. In this review, we present an overview of various existing models within the field, examining their strengths and limitations, particularly in the context of neuropathic pain research. We illustrate the usefulness of these models through a comparative review in three crucial areas: i) the development of novel phenotypic screening strategies specifically for neuropathic pain, ii) the validation of the models for both primary and secondary screening assays, and iii) the use of the models in target deconvolution processes.

Effects of Regulating Hippo and Wnt on the Development and Fate Differentiation of Bovine Embryo

The improvement of in vitro embryo development is a gateway to enhance the output of assisted reproductive technologies. The Wnt and Hippo signaling pathways are crucial for the early development of bovine embryos. This study investigated the development of bovine embryos under the influence of a Hippo signaling agonist (LPA) and a Wnt signaling inhibitor (DKK1). In this current study, embryos produced in vitro were cultured in media supplemented with LPA and DKK1. We comprehensively analyzed the impact of LPA and DKK1 on various developmental parameters of the bovine embryo, such as blastocyst formation, differential cell counts, YAP fluorescence intensity and apoptosis rate. Furthermore, single-cell RNA sequencing (scRNA-seq) was employed to elucidate the in vitro embryonic development. Our results revealed that LPA and DKK1 improved the blastocyst developmental potential, total cells, trophectoderm (TE) cells and YAP fluorescence intensity and decreased the apoptosis rate of bovine embryos. A total of 1203 genes exhibited differential expression between the control and LPA/DKK1-treated (LD) groups, with 577 genes upregulated and 626 genes downregulated. KEGG pathway analysis revealed significant enrichment of differentially expressed genes (DEGs) associated with TGF-beta signaling, Wnt signaling, apoptosis, Hippo signaling and other critical developmental pathways. Our study shows the role of LPA and DKK1 in embryonic differentiation and embryo establishment of pregnancy. These findings should be helpful for further unraveling the precise contributions of the Hippo and Wnt pathways in bovine trophoblast formation, thus advancing our comprehension of early bovine embryo development.

DELVE: feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .

Dynamic video recognition for cell-encapsulating microfluidic droplets

Droplet microfluidics is a highly sensitive and high-throughput technology extensively utilized in biomedical applications, such as single-cell sequencing and cell screening. However, its performance is highly influenced by the droplet size and single-cell encapsulation rate (following random distribution), thereby creating an urgent need for quality control. Machine learning has the potential to revolutionize droplet microfluidics, but it requires tedious pixel-level annotation for network training. This paper investigates the application software of the weakly supervised cell-counting network (WSCApp) for video recognition of microdroplets. We demonstrated its real-time performance in video processing of microfluidic droplets and further identified the locations of droplets and encapsulated cells. We verified our methods on droplets encapsulating six types of cells/beads, which were collected from various microfluidic structures. Quantitative experimental results showed that our approach can not only accurately distinguish droplet encapsulations (micro-F1 score > 0.94), but also locate each cell without any supervised location information. Furthermore, fine-tuning transfer learning on the pre-trained model also significantly reduced (>80%) annotation. This software provides a user-friendly and assistive annotation platform for the quantitative assessment of cell-encapsulating microfluidic droplets.

CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues

Motivation

Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples enable the benchmark of existing methods.

Results

In this study, we conduct a comprehensive assessment of 31 methods, utilizing single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the critical assessment of transcriptomic deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods.

Availability and implementation

https://github.com/Papatheodorou-Group/CATD_snakemake.

A high-speed microscopy system based on deep learning to detect yeast-like fungi cells in blood

Background: Blood-invasive fungal infections can cause the death of patients, while diagnosis of fungal infections is challenging. Methods: A high-speed microscopy detection system was constructed that included a microfluidic system, a microscope connected to a high-speed camera and a deep learning analysis section. Results: For training data, the sensitivity and specificity of the convolutional neural network model were 93.5% (92.7-94.2%) and 99.5% (99.1-99.5%), respectively. For validating data, the sensitivity and specificity were 81.3% (80.0-82.5%) and 99.4% (99.2-99.6%), respectively. Cryptococcal cells were found in 22.07% of blood samples. Conclusion: This high-speed microscopy system can analyze fungal pathogens in blood samples rapidly with high sensitivity and specificity and can help dramatically accelerate the diagnosis of fungal infectious diseases.

Comparative analysis of cell-cell communication at single-cell resolution

Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.

Development and future of droplet microfluidics

Over the past two decades, advances in droplet-based microfluidics have facilitated new approaches to process and analyze samples with unprecedented levels of precision and throughput. A wide variety of applications has been inspired across multiple disciplines ranging from materials science to biology. Understanding the dynamics of droplets enables optimization of microfluidic operations and design of new techniques tailored to emerging demands. In this review, we discuss the underlying physics behind high-throughput generation and manipulation of droplets. We also summarize the applications in droplet-derived materials and droplet-based lab-on-a-chip biotechnology. In addition, we offer perspectives on future directions to realize wider use of droplet microfluidics in industrial production and biomedical analyses.

Microfluidic-assisted single-cell RNA sequencing facilitates the development of neutralizing monoclonal antibodies against SARS-CoV-2

As a class of antibodies that specifically bind to a virus and block its entry, neutralizing monoclonal antibodies (neutralizing mAbs) have been recognized as a top choice for combating COVID-19 due to their high specificity and efficacy in treating serious infections. Although conventional approaches for neutralizing mAb development have been optimized for decades, there is an urgent need for workflows with higher efficiency due to time-sensitive concerns, including the high mutation rate of SARS-CoV-2. One promising approach is the identification of neutralizing mAb candidates via single-cell RNA sequencing (RNA-seq), as each B cell has a unique transcript sequence corresponding to its secreted antibody. The state-of-the-art high-throughput single-cell sequencing technologies, which have been greatly facilitated by advances in microfluidics, have greatly accelerated the process of neutralizing mAb development. Here, we provide an overview of the general procedures for high-throughput single-cell RNA-seq enabled by breakthroughs in droplet microfluidics, introduce revolutionary approaches that combine single-cell RNA-seq to facilitate the development of neutralizing mAbs against SARS-CoV-2, and outline future steps that need to be taken to further improve development strategies for effective treatments against infectious diseases.

Pan-Cancer Single-Nucleus Total RNA Sequencing Using snHH-Seq

Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA-seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high-throughput and high-sensitivity method called snHH-seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full-length RNA-seq data is also established. snHH-seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full-length RNA at the single-nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.

Magnetically functionalized hydrogels for high-throughput genomic applications

Single-cell genomics has revolutionized tissue analysis by revealing the genetic program of individual cells. The key aspect of the technology is the use of barcoded beads to unambiguously tag sequences originating from a single cell. The generation of unique barcodes on beads is mainly achieved by split-pooling methods, which are labor-intensive due to repeated washing steps. Towards the automation of the split-pooling method, we developed a simple method to magnetize hydrogel beads. We show that these hydrogel beads provide increased yields and washing efficiencies for purification procedures. They are also fully compatible with single-cell sequencing using the BAG-Seq workflow. Our work opens the automation of the split-pooling technique, which will improve single-cell genomic workflows.

Tailored Fluorosurfactants through Controlled/Living Radical Polymerization for Highly Stable Microfluidic Droplet Generation

Droplet-based microfluidics represents a disruptive technology in the field of chemistry and biology through the generation and manipulation of sub-microlitre droplets. To avoid droplet coalescence, fluoropolymer-based surfactants are commonly used to reduce the interfacial tension between two immiscible phases to stabilize droplet interfaces. However, the conventional preparation of fluorosurfactants involves multiple steps of conjugation reactions between fluorinated and hydrophilic segments to form multiple-block copolymers. In addition, synthesis of customized surfactants with tailored properties is challenging due to the complex synthesis process. Here, we report a highly efficient synthetic method that utilizes living radical polymerization (LRP) to produce fluorosurfactants with tailored functionalities. Compared to the commercialized surfactant, our surfactants outperform in thermal cycling for polymerase chain reaction (PCR) testing, and exhibit exceptional biocompatibility for cell and yeast culturing in a double-emulsion system. This breakthrough synthetic approach has the potential to revolutionize the field of droplet-based microfluidics by enabling the development of novel designs that generate droplets with superior stability and functionality for a wide range of applications.

Design automation of microfluidic single and double emulsion droplets with machine learning

Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to specialist labs. Here, we compile a comprehensive droplet dataset and use it to train machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable aqueous-in-oil and oil-in-aqueous single and double emulsions from 15 to 250 μm at rates up to 12000 Hz for different fluids commonly used in life sciences. Blind predictions by our models for as-yet-unseen fluids, geometries, and device materials yield accurate results, establishing their generalizability. Finally, we generate an easy-to-use design automation tool that yield droplets within 3 μm (<8%) of the desired diameter, facilitating tailored droplet-based platforms and accelerating their utility in life sciences.

Single-cell transcriptomics reveals ferrimagnetic vortex iron oxide nanoring-mediated mild magnetic hyperthermia exerts antitumor effects by alleviating macrophage suppression in breast cancer

The potential of Ferrimagnetic vortex iron oxide nanoring-mediated mild magnetic hyperthermia (FVIO-MHT) in solid tumor therapy has been demonstrated. However, the impact of FVIO-MHT on the tumor microenvironment (TME) remains unclear. This study utilized single-cell transcriptome sequencing to examine the alterations in the TME in response to FVIO-MHT in breast cancer. The results revealed the cellular composition within the tumor microenvironment (TME) was primarily modified due to a decrease in tumor cells and an increased infiltration of myeloid cells. Subsequently, an enhancement in active oxygen (ROS) metabolism was observed, indicating oxidative damage to tumor cells. Interestingly, FVIO-MHT reprogrammed the macrophages' phenotypes, as evidenced by alterations in the transcriptome characteristics associated with both classic and alternative activated phenotypes. And an elevated level of ROS generation and oxidative phosphorylation suggested that activated phagocytosis and inflammation occurred in macrophages. Additionally, cell-cell communication analysis revealed that FVIO-MHT attenuated the suppression between tumor cells and macrophages by inhibiting phagocytic checkpoint and macrophage migration inhibitory factor signaling pathways. Inhibition of B2m, an anti-phagocytosis checkpoint, could promote macrophage-mediated phagocytosis and significantly inhibit tumor growth. These data emphasize FVIO-MHT may promote the antitumor capabilities of macrophages by alleviating the suppression between tumor cells and macrophages.

Single-Cell Sequencing of 3' RNA Transcripts

Single-cell RNA sequencing (scRNA-seq) enables the measurement of RNA expressed from individual cells within a tissue or population. RNA expression profiles may be used to draw conclusions about cellular states, cell subtypes within the population, responses to perturbations, and cellular behavior in the context of disease. Here we describe a method for scRNA-seq via single-cell encapsulation and capture of the polyadenosine tails at the 3' end of mRNA transcripts combined with cell and molecular barcoding, allowing for the sequencing of 3' untranslated regions in order to identify expressed genes from a cell.

Methods and Measures for Investigating Microscale Motility

Motility is an essential factor for an organism's survival and diversification. With the advent of novel single-cell technologies, analytical frameworks, and theoretical methods, we can begin to probe the complex lives of microscopic motile organisms and answer the intertwining biological and physical questions of how these diverse lifeforms navigate their surroundings. Herein, we summarize the main mechanisms of microscale motility and give an overview of different experimental, analytical, and mathematical methods used to study them across different scales encompassing the molecular-, individual-, to population-level. We identify transferable techniques, pressing challenges, and future directions in the field. This review can serve as a starting point for researchers who are interested in exploring and quantifying the movements of organisms in the microscale world.

Improving the RNA velocity approach with single-cell RNA lifecycle (nascent, mature and degrading RNAs) sequencing technologies

We presented an experimental method called FLOUR-seq, which combines BD Rhapsody and nanopore sequencing to detect the RNA lifecycle (including nascent, mature, and degrading RNAs) in cells. Additionally, we updated our HIT-scISOseq V2 to discover a more accurate RNA lifecycle using 10x Chromium and Pacbio sequencing. Most importantly, to explore how single-cell full-length RNA sequencing technologies could help improve the RNA velocity approach, we introduced a new algorithm called 'Region Velocity' to more accurately configure cellular RNA velocity. We applied this algorithm to study spermiogenesis and compared the performance of FLOUR-seq with Pacbio-based HIT-scISOseq V2. Our findings demonstrated that 'Region Velocity' is more suitable for analyzing single-cell full-length RNA data than traditional RNA velocity approaches. These novel methods could be useful for researchers looking to discover full-length RNAs in single cells and comprehensively monitor RNA lifecycle in cells.

Stem cell heterogeneity, plasticity, and regulation

As a population of homogeneous cells with both self-renewal and differentiation potential, stem cell pools are highly compartmentalized and contain distinct subsets that exhibit stable but limited heterogeneity during homeostasis. However, their striking plasticity is showcased under natural or artificial stress, such as injury, transplantation, cancer, and aging, leading to changes in their phenotype, constitution, metabolism, and function. The complex and diverse network of cell-extrinsic niches and signaling pathways, together with cell-intrinsic genetic and epigenetic regulators, tightly regulate both the heterogeneity during homeostasis and the plasticity under perturbation. Manipulating these factors offers better control of stem cell behavior and a potential revolution in the current state of regenerative medicine. However, disruptions of normal regulation by genetic mutation or excessive plasticity acquisition may contribute to the formation of tumors. By harnessing innovative techniques that enhance our understanding of stem cell heterogeneity and employing novel approaches to maximize the utilization of stem cell plasticity, stem cell therapy holds immense promise for revolutionizing the future of medicine.

Irisin reduces amyloid-β by inducing the release of neprilysin from astrocytes following downregulation of ERK-STAT3 signaling

A pathological hallmark of Alzheimer's disease (AD) is the deposition of amyloid-β (Aβ) protein in the brain. Physical exercise has been shown to reduce Aβ burden in various AD mouse models, but the underlying mechanisms have not been elucidated. Irisin, an exercise-induced hormone, is the secreted form of fibronectin type-III-domain-containing 5 (FNDC5). Here, using a three-dimensional (3D) cell culture model of AD, we show that irisin significantly reduces Aβ pathology by increasing astrocytic release of the Aβ-degrading enzyme neprilysin (NEP). This is mediated by downregulation of ERK-STAT3 signaling. Finally, we show that integrin αV/β5 acts as the irisin receptor on astrocytes required for irisin-induced release of astrocytic NEP, leading to clearance of Aβ. Our findings reveal for the first time a cellular and molecular mechanism by which exercise-induced irisin attenuates Aβ pathology, suggesting a new target pathway for therapies aimed at the prevention and treatment of AD.

Programmable Control of Nanoliter Droplet Arrays using Membrane Displacement Traps

A unique droplet microfluidic technology enabling programmable deterministic control over complex droplet operations is presented. The platform provides software control over user-defined combinations of droplet generation, capture, ejection, sorting, splitting, and merging sequences to enable the design of flexible assays employing nanoliter-scale fluid volumes. The system integrates a computer vision system with an array of membrane displacement traps capable of performing selected unit operations with automated feedback control. Sequences of individual droplet handling steps are defined through a robust Python-based scripting language. Bidirectional flow control within the microfluidic chips is provided using an H-bridge channel topology, allowing droplets to be transported to arbitrary trap locations within the array for increased operational flexibility. By enabling automated software control over all droplet operations, the system significantly expands the potential of droplet microfluidics for diverse biological and biochemical applications by combining the functionality of robotic liquid handling with the advantages of droplet-based fluid manipulation.

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

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

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Primary cilia-mediated regulation of microglial secretion in Alzheimer's disease

Alzheimer's disease (AD) is a brain disorder manifested by a gradual decline in cognitive function due to the accumulation of extracellular amyloid plaques, disruptions in neuronal substance transport, and the degeneration of neurons. In affected neurons, incomplete clearance of toxic proteins by neighboring microglia leads to irreversible brain inflammation, for which cellular signaling is poorly understood. Through single-cell transcriptomic analysis, we discovered distinct regional differences in the ability of microglia to clear damaged neurites. Specifically, microglia in the septal region of wild type mice exhibited a transcriptomic signature resembling disease-associated microglia (DAM). These lateral septum (LS)-enriched microglia were associated with dense axonal bundles originating from the hippocampus. Further transcriptomic and proteomic approaches revealed that primary cilia, small hair-like structures found on cells, played a role in the regulation of microglial secretory function. Notably, primary cilia were transiently observed in microglia, and their presence was significantly reduced in microglia from AD mice. We observed significant changes in the secretion and proteomic profiles of the secretome after inhibiting the primary cilia gene intraflagellar transport particle 88 (Ift88) in microglia. Intriguingly, inhibiting primary cilia in the septal microglia of AD mice resulted in the expansion of extracellular amyloid plaques and damage to adjacent neurites. These results indicate that DAM-like microglia are present in the LS, a critical target region for hippocampal nerve bundles, and that the primary ciliary signaling system regulates microglial secretion, affecting extracellular proteostasis. Age-related primary ciliopathy probably contributes to the selective sensitivity of microglia, thereby exacerbating AD. Targeting the primary ciliary signaling system could therefore be a viable strategy for modulating neuroimmune responses in AD treatments.

Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease

Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research and the principles of genomics, transcriptomics, and epigenomics, alongside their most advanced technologies (like single-cell omics and spatial omics), which allow for the investigation of the molecular events that occur at a single-cell resolution while retaining the spatial information.

scIBD: a self-supervised iterative-optimizing model for boosting the detection of heterotypic doublets in single-cell chromatin accessibility data

Application of the widely used droplet-based microfluidic technologies in single-cell sequencing often yields doublets, introducing bias to downstream analyses. Especially, doublet-detection methods for single-cell chromatin accessibility sequencing (scCAS) data have multiple assay-specific challenges. Therefore, we propose scIBD, a self-supervised iterative-optimizing model for boosting heterotypic doublet detection in scCAS data. scIBD introduces an adaptive strategy to simulate high-confident heterotypic doublets and self-supervise for doublet-detection in an iteratively optimizing manner. Comprehensive benchmarking on various simulated and real datasets demonstrates the outperformance and robustness of scIBD. Moreover, the downstream biological analyses suggest the efficacy of doublet-removal by scIBD.

Research progress on the application of single-cell sequencing in autoimmune diseases

Autoimmune diseases (AIDs) are caused by immune tolerance deficiency or abnormal immune regulation, leading to damage to host organs. The complicated pathogenesis and varied clinical symptoms of AIDs pose great challenges in diagnosing and monitoring this disease. Regrettably, the etiological factors and pathogenesis of AIDs are still not completely understood. It is noteworthy that the development of single-cell RNA sequencing (scRNA-seq) technology provides a new tool for analyzing the transcriptome of AIDs. In this essay, we have summarized the development of scRNA-seq technology, and made a relatively systematic review of the current research progress of scRNA-seq technology in the field of AIDs, providing a reference to preferably understand the pathogenesis, diagnosis, and treatment of AIDs.

White Adipose Tissue Heterogeneity in the Single-Cell Era: From Mice and Humans to Cattle

Adipose tissue is a major modulator of metabolic function by regulating energy storage and by acting as an endocrine organ through the secretion of adipokines. With the advantage of next-generation sequencing-based single-cell technologies, adipose tissue has been studied at single-cell resolution, thus providing unbiased insight into its molecular composition. Recent single-cell RNA sequencing studies in human and mouse models have dissected the transcriptional cellular heterogeneity of subcutaneous (SAT), visceral (VAT), and intramuscular (IMAT) white adipose tissue depots and revealed unique populations of adipose tissue progenitor cells, mature adipocytes, immune cell, vascular cells, and mesothelial cells that play direct roles on adipose tissue function and the development of metabolic disorders. In livestock species, especially in bovine, significant gaps of knowledge remain in elucidating the roles of adipose tissue cell types and depots on driving the pathogenesis of metabolic disorders and the distinct fat deposition in VAT, SAT, and IMAT in meat animals. This review summarizes the current knowledge on the transcriptional and functional cellular diversity of white adipose tissue revealed by single-cell approaches and highlights the depot-specific function of adipose tissue in different mammalian species, with a particular focus on recent findings and future implications in cattle.

Large-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models

Deep learning-based markerless tracking has revolutionized studies of animal behavior. Yet the generalizability of trained models tends to be limited, as new training data typically needs to be generated manually for each setup or visual environment. With each model trained from scratch, researchers track distinct landmarks and analyze the resulting kinematic data in idiosyncratic ways. Moreover, due to inherent limitations in manual annotation, only a sparse set of landmarks are typically labeled. To address these issues, we developed an approach, which we term GlowTrack, for generating orders of magnitude more training data, enabling models that generalize across experimental contexts. We describe: a) a high-throughput approach for producing hidden labels using fluorescent markers; b) a multi-camera, multi-light setup for simulating diverse visual conditions; and c) a technique for labeling many landmarks in parallel, enabling dense tracking. These advances lay a foundation for standardized behavioral pipelines and more complete scrutiny of movement.

Desmosome mutations impact the tumor microenvironment to promote melanoma proliferation

Desmosomes are transmembrane protein complexes that contribute to cell-cell adhesion in epithelia and other tissues. Here, we report the discovery of frequent genetic alterations in the desmosome in human cancers, with the strongest signal seen in cutaneous melanoma where desmosomes are mutated in over 70% of cases. In primary but not metastatic melanoma biopsies, the burden of coding mutations on desmosome genes associates with a strong reduction in desmosome gene expression. Analysis by spatial transcriptomics suggests that these expression decreases occur in keratinocytes in the microenvironment rather than in primary melanoma tumor cells. In further support of a microenvironmental origin, we find that loss-of-function knockdowns of the desmosome in keratinocytes yield markedly increased proliferation of adjacent melanocytes in keratinocyte/melanocyte co-cultures. Thus, gradual accumulation of desmosome mutations in neighboring cells may prime melanocytes for neoplastic transformation.

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.

Deep learning applications in single-cell genomics and transcriptomics data analysis

Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.

Droplet-based transcriptome profiling of individual synapses

Synapses are crucial structures that mediate signal transmission between neurons in complex neural circuits and display considerable morphological and electrophysiological heterogeneity. So far we still lack a high-throughput method to profile the molecular heterogeneity among individual synapses. In the present study, we develop a droplet-based single-cell (sc) total-RNA-sequencing platform, called Multiple-Annealing-and-Tailing-based Quantitative scRNA-seq in Droplets, for transcriptome profiling of individual neurites, primarily composed of synaptosomes. In the synaptosome transcriptome, or 'synaptome', profiling of both mouse and human brain samples, we detect subclusters among synaptosomes that are associated with neuronal subtypes and characterize the landscape of transcript splicing that occurs within synapses. We extend synaptome profiling to synaptopathy in an Alzheimer's disease (AD) mouse model and discover AD-associated synaptic gene expression changes that cannot be detected by single-nucleus transcriptome profiling. Overall, our results show that this platform provides a high-throughput, single-synaptosome transcriptome profiling tool that will facilitate future discoveries in neuroscience.