Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells

Unveiling the spatiotemporal strategies of plants in response to biotic and abiotic stresses:A comprehensive review

Plant functions are governed by complex regulatory mechanisms that operate across diverse cell types in various tissues. However, the challenge of dissecting plant tissues has hindered the widespread application of single-cell technologies in plant research. Recent advancements in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have propelled the field forward. scRNA-seq enables the examination of gene expression at the single-cell level, while ST preserves the spatial context of cellular organization. While previous reviews have discussed the breakthroughs of scRNA-seq and ST in plants, none have comprehensively addressed the use of these technologies to study plant responses to environmental stress at the cellular level. This review provides an in-depth analysis of the development, advantages, and limitations of single-cell and spatial transcriptomics, highlighting their critical role in unraveling plant strategies for coping with biotic and abiotic stresses. We also explore the challenges and future prospects of integrating scRNA-seq and ST in plant research. Understanding cell-specific responses and the complex interactions between cellular entities within the plant under stress is essential for advancing our knowledge of plant biology.

A comprehensive understanding on droplets

Droplets are ubiquitous and necessary in natural phenomena, daily life, and industrial processes, which play a crucial role in many fields. So, the manipulation of droplets has been extensively investigated for meeting widespread applications, consequently, a great deal of progresses have been achieved across multiple disciplines ranging from chemistry to physics, material, biological, and energy science. For example, microdroplets have been utilized as reactors, colorimetric or electrochemical sensors, drug-delivery carriers, and energy harvesters. Moreover, droplet manipulation is the basis in both fundamental researches and practical applications, especially the combination of smart materials and external fields for achieving multifunctional applications of droplets. In view of this background, this review initiates discussion of the manipulation strategies of droplets including Laplace pressure, wettability gradients, electric field, magnetic force, light and temperature. Thereafter, based on their manipulation strategies, this review mainly summarizes the applications of droplets in the fields of robot, green energy, sensors, biomedical treatments, microreactors and chemical reactions. Application related basic concepts, theories, principles and progresses also have been introduced. Finally, this review addresses the challenges of manipulation and applications of droplets and provides the potential directions for their future development. By presenting these results, we aim to provide a comprehensive overview of water droplets and establish a unified framework that guides the development of droplets in various fields.

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.

Droplet Microfluidics for Advanced Single-Cell Analysis

Droplet microfluidics has emerged as a breakthrough technology that is changing our comprehension of single-cell and their associated research. By separating individual cells within tiny droplets, ranging from nanoliters to picoliters using microfluidic devices, this innovative approach has revolutionized investigations at the single-cell level. Each of these droplets serves as a distinct experimental reaction vessel, enabling thorough exploration of cellular phenotypic variations, interactions between cells or cell-microorganisms as well as genomic insights. This review paper presents a comprehensive overview of the current state-of-the-art in droplet microfluidics, which has made single-cell analysis a practical approach for biological research. The review delves into the technological advancements in single-cell encapsulation techniques within droplet microfluidics, elucidating their applications in high-throughput single-cell screening, intercellular and cell-microorganism interactions, and genomic analysis. Furthermore, it discusses the advantages and constraints of droplet microfluidic technology, shedding light on critical factors such as throughput and versatile integration. Lastly, the paper outlines the potential avenues for future research in this rapidly evolving field.

Mitigating Cell Cycle Effects in Multi-Omics Data: Solutions and Analytical Frameworks

Cell cycle structures vary significantly across cell types, which exhibit distinct phase compositions. Asynchronous DNA replication and dynamic cellular characteristics during the cell cycle result in considerable heterogeneity in DNA dosage, chromatin accessibility, methylation, and expression. Nonetheless, the consequences of cell cycle disruption in the interpretation of multi-omics data remain unclear. Here, we systematically assessed the influence of distinct cell phase structures on the interpretation of omics features in proliferating cells, and proposed solutions for each omics dataset. For copy number variation (CNV) calling, asynchronous replication timing (RT) interference induces false CNVs in cells with high S-phase ratio (SPR), which are significantly decreased following replication timing domain (RTD) correction. Similar noise is observed in the chromatin accessibility data. Moreover, for DNA methylation and transcriptomic analyses, cell cycle-sorted data outperformed direct comparison in elucidating the biological features of compared cells. Additionally, we established an integrated pipeline to identify differentially expressed genes (DEGs) after cell cycle phasing. Consequently, our study demonstrated extensive cell-cycle heterogeneity, warranting consideration in future studies involving cells with diverse cell-cycle structures. RTD correction or phase-specific comparison could reduce the influence of cell cycle composition on the analysis of the differences observed between stem and differentiated cells.

scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling

BACKGROUND

Single-cell RNA sequencing (scRNA-Seq) technology reveals biological processes and molecular-level genomic information among individual cells. Numerous computational methods, including methods based on graph neural networks (GNNs), have been developed to enhance scRNA-Seq data analysis. However, existing GNNs-based methods usually construct fixed graphs by applying the k-nearest neighbors algorithm, which may result in information loss.

METHODS

To address this problem, we propose scE2EGAE, which learns cell graphs during the training processes. Firstly, the scRNA-Seq data is fed into a deep count autoencoder (DCA). Secondly, the hidden representations of DCA are extracted and then used to generate cell-to-cell graph edges through a straight-through estimator (STE) based on top-k sampling and Gumbel-Softmax. Finally, the generated cell-to-cell graph and scRNA-Seq data are fed into the GNNs-based downstream tasks. In this paper, we design a graph autoencoder which performs denoising on scRNA-Seq data as the downstream task.

RESULTS

We evaluate scE2EGAE on eight public scRNA-Seq datasets and compare its performance with seven existing scRNA-Seq data denoising methods. In this paper, extensive experiments are conducted, encompassing: 1) the evaluation of denoising performance, with metrics including mean absolute error, Pearson correlation coefficient, and cosine similarity; 2) the assessment of clustering performance of the denoised results, utilizing adjusted rand index, normalized mutual information and silhouette score; and 3) the evaluation of the cell trajectory inference performance of the denoised results, measured by the pseudo-temporal ordering score. The results show that, on the scRNA-Seq data denoising task, scE2EGAE outperforms most of the methods, proving that it can learn cell-to-cell graphs containing real information of cell-to-cell relationships.

CONCLUSIONS

In this paper, we validate the proposed scE2EGAE method through its application to the denoising task of scRNA-Seq data. This method demonstrates its capability to learn inter-cellular relationships and construct cell-to-cell graphs, thereby enhancing the downstream analysis of scRNA-Seq data. Our approach can serve as an inspiration for future research on scRNA-Seq analysis methods based on GNNs, holding broad application prospects.

A robust multi-scale clustering framework for single-cell RNA-seq data analysis

Recent advancements in single-cell RNA sequencing (scRNA-seq) technology have unlocked novel opportunities for deep exploration of gene expression patterns. However, the inherent high dimensionality, sparsity, and noise in scRNA-seq data pose significant challenges for existing clustering methods, especially in accurately identifying and classifying diverse cell types. To address these challenges, we introduce a new method, single-cell Multi-Scale Clustering Framework (scMSCF), which combines multi-dimensional PCA for dimensionality reduction, K-means clustering, and a weighted ensemble meta-clustering approach, enhanced by a self-attention-driven Transformer model to optimize clustering performance. scMSCF constructs an initial clustering framework using a multi-layer dimensionality reduction strategy to establish a robust consensus on clustering structure. A voting mechanism within the meta-clustering process selects high-confidence cells from the initial clustering results to provide precise training labels for the Transformer model. This approach enables the model to capture complex dependencies in gene expression data, thereby enhancing clustering accuracy. Comprehensive testing across eight single-cell RNA sequencing datasets demonstrates that scMSCF surpasses existing methods, achieving on average 10-15% higher ARI, NMI, and ACC scores. For example, on the PBMC5k dataset, scMSCF improves ARI from 0.72 to 0.86, demonstrating its ability to accurately identify diverse cell populations. The source code for our algorithm is publicly available at https://github.com/DEREKJ24/scMSCF .

Single microorganism RNA sequencing of microbiomes using smRandom-Seq

Bacteria colonize nearly every part of the human body and various environments, displaying remarkable diversity. Traditional population-level transcriptomics measurements provide only average population behaviors, often overlooking the heterogeneity within bacterial communities. To address this limitation, we have developed a droplet-based, high-throughput single-microorganism RNA sequencing method (smRandom-seq) that offers highly species specific and sensitive gene detection. Here we detail procedures for microbial sample preprocessing, in situ preindexed cDNA synthesis, in situ poly(dA) tailing, droplet barcoding, ribosomal RNA depletion and library preparation. The main smRandom-seq workflow, including sample processing, in situ reactions and library construction, takes ~2 days. This method features enhanced RNA coverage, reduced doublet rates and minimized ribosomal RNA contamination, thus enabling in-depth analysis of microbial heterogeneity. smRandom-seq is compatible with microorganisms from both laboratory cultures and complex microbial community samples, making it well suited for constructing single-microorganism transcriptomic atlases of bacterial strains and diverse microbial communities. This Protocol requires experience in molecular biology and RNA sequencing techniques, and it holds promising potential for researchers investigating bacterial resistance, microbiome heterogeneity and host-microorganism interactions.

SMOPCA: spatially aware dimension reduction integrating multi-omics improves the efficiency of spatial domain detection

Technological advances have enabled us to profile multiple omics layers with spatial information, significantly enhancing spatial domain detection and advancing a variety of biomedical research fields. Despite these advancements, there is a notable lack of effective methods for modeling spatial multi-omics data. We introduce SMOPCA, a Spatial Multi-Omics Principal Component Analysis method designed to perform joint dimension reduction on multimodal data while preserving spatial dependencies. Extensive experiments reveal that SMOPCA outperforms existing single-modal and multimodal dimension reduction and clustering methods, across both single-cell and spatial multi-omics datasets derived from diverse technologies and tissue structures.

A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics

Single-cell RNA-seq (scRNAseq) struggles to capture the cellular heterogeneity of transcripts within individual cells due to the prevalence of highly abundant and ubiquitous transcripts, which can obscure the detection of biologically distinct transcripts expressed up to several orders of magnitude lower levels. To address this challenge, here we introduce single-cell CRISPRclean (scCLEAN), a molecular method that globally recomposes scRNAseq libraries, providing a benefit that cannot be recapitulated with deeper sequencing. scCLEAN utilizes the programmability of CRISPR/Cas9 to target and remove less than 1% of the transcriptome while redistributing approximately half of reads, shifting the focus toward less abundant transcripts. We experimentally apply scCLEAN to both heterogeneous immune cells and homogenous vascular smooth muscle cells to demonstrate its ability to uncover biological signatures in different biological contexts. We further emphasize scCLEAN's versatility by applying it to a third-generation sequencing method, single-cell MAS-Seq, to increase transcript-level detection and discovery. Here we show the possible utility of scCLEAN across a wide array of human tissues and cell types, indicating which contexts this technology proves beneficial and those in which its application is not advisable.

Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications

Single-cell RNA-sequencing (scRNA-seq) technology brought about a revolutionary change in the transcriptomic world, paving the way for comprehensive analysis of cellular heterogeneity in complex biological systems. It enabled researchers to see how different cells behaved at single-cell levels, providing new insights into the process. However, despite all these advancements, scRNA-seq also experiences challenges related to the complexity of data analysis, interpretation, and multi-omics data integration. In this review, these complications were discussed in detail, directly pointing at the optimization of scRNA-seq approaches and understanding the world of single-cell and its dynamics. Different protocols and currently functional single-cell databases were also covered. This review highlights different tools for the analysis of scRNA-seq and their methodologies, emphasizing innovative techniques that enhance resolution and accuracy at a single-cell level. Various applications were explored across domains including drug discovery, tumor microenvironment (TME), biomarker discovery, and microbial profiling, and case studies were discussed to explain the importance of scRNA-seq by uncovering novel and rare cell types and their identification. This review underlines a crucial aspect of scRNA-seq in the advancement of personalized medicine and highlights its potential to understand the complexity of biological systems.

Biochemical Impact of p300-Mediated Acetylation of Replication Protein A: Implications for DNA Metabolic Pathway Choice

Replication Protein A (RPA), a single-stranded DNA (ssDNA) binding protein, is vital for various aspects of genome maintenance such as replication, recombination, repair and cell cycle checkpoint activation. Binding of RPA to ssDNA protects it from degradation by cellular nucleases, prevents secondary structure formation and suppresses illegitimate recombination. In our current study, we identified the acetyltransferase p300 to be capable of acetylating the 70kDa subunit of RPA in vitro and within cells. The acetylation status of RPA changes throughout the cell cycle, increasing during the S and G2/M phases, and after UV-induced damage. Furthermore, we were able to specifically identify RPA directly associated with the replication fork during the S phase and UV damage to be acetylated. Based on these observations, we evaluated the impact of lysine acetylation on the biochemical properties of RPA. Investigation of binding properties of RPA revealed that acetylation of RPA increased its binding affinity to ssDNA compared to unmodified RPA. The improvement in binding efficiency was a function of DNA length with the greatest increases observed on shorter length ssDNA oligomers. Enzymatic assays further revealed that upon acetylation RPA governs the switch between the short and long flap pathway for Okazaki fragment processing. Our findings demonstrate that p300-dependent, site-specific acetylation enhances RPA's DNA binding properties, potentially regulating its function during various DNA transactions.

Temporally discordant chromatin accessibility and DNA demethylation define short- and long-term enhancer regulation during cell fate specification

Chromatin and DNA modifications mediate the transcriptional activity of lineage-specifying enhancers, but recent work challenges the dogma that joint chromatin accessibility and DNA demethylation are prerequisites for transcription. To understand this paradox, we established a highly resolved timeline of their dynamics during neural progenitor cell differentiation. We discovered that, while complete demethylation appears delayed relative to shorter-lived chromatin changes for thousands of enhancers, DNA demethylation actually initiates with 5-hydroxymethylation before appreciable accessibility and transcription factor occupancy is observed. The extended timeline of DNA demethylation creates temporal discordance appearing as heterogeneity in enhancer regulatory states. Few regions ever gain methylation, and resulting enhancer hypomethylation persists long after chromatin activities have dissipated. We demonstrate that the temporal methylation status of CpGs (mC/hmC/C) predicts past, present, and future chromatin accessibility using machine learning models. Thus, chromatin and DNA methylation collaborate on different timescales to shape short- and long-term enhancer regulation during cell fate specification.

High-throughput strategies for monoclonal antibody screening: advances and challenges

Antibodies characterized by high affinity and specificity, developed through high-throughput screening and rapid preparation, are crucial to contemporary biomedical industry. Traditional antibody preparation via the hybridoma strategy faces challenges like low efficiency, long manufacturing cycles, batch variability and labor intensity. Advances in molecular biology and gene editing technologies offer revolutionary improvements in antibody production. New high-throughput technologies like antibody library display, single B cell antibody technologies, and single-cell sequencing have significantly cut costs and boosted the efficiency of antibody development. These innovations accelerate commercial applications of antibodies, meeting the biopharmaceutical industry's evolving demands. This review explores recent advancements in high-throughput development of antibody, highlighting their potential advantages over traditional methods and their promising future.

Using Single-Cell RNA sequencing with Drosophila, Zebrafish, and mouse models for studying Alzheimer's and Parkinson's disease

Alzheimer's and Parkinson's disease are the most common neurodegenerative diseases, significantly affecting the elderly with no current cure available. With the rapidly aging global population, advancing research on these diseases becomes increasingly critical. Both disorders are often studied using model organisms, which enable researchers to investigate disease phenotypes and their underlying molecular mechanisms. In this review, we critically discuss the strengths and limitations of using Drosophila, zebrafish, and mice as models for Alzheimer's and Parkinson's research. A focus is the application of single-cell RNA sequencing, which has revolutionized the field by providing novel insights into the cellular and transcriptomic landscapes characterizing these diseases. We assess how combining animal disease modeling with high-throughput sequencing and computational approaches has advanced the field of Alzheimer's and Parkinson's disease research. Thereby, we highlight the importance of integrative multidisciplinary approaches to further our understanding of disease mechanisms and thus accelerating the development of successful therapeutic interventions.

Every cell every gene all at once: Systems genetic approaches toward corticogenesis

The development of the cerebral cortex is a stepwise process that involves numerous cell types and signaling pathways to achieve the functional assembly of neural circuits. Our understanding of this process is primarily rooted in findings from studying transgenic knockout models, which reveal coordinated molecular actions, particularly transcription factor cascades critical for cell type acquisition and maintenance in a context-dependent manner. Further resolving their cell type specificity necessitates the use of high-throughput, high-content methodologies. Over the past decade, the emerging single-cell genomics and in vivo CRISPR tools have provided new approaches to study neurodevelopment with elevated scale and resolution. In this review, we discussed efforts to study mouse cortical cell fate determination using single-cell genomics methods. Additionally, we explored recent studies combining programmable gene editing with single-cell phenotypic assays to investigate the function of transcription factors in perinatal cortical development, delineating cell-type specific, functional cytoarchitecture of the developing brain.

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.

Heterogeneity of the tumor immune cell microenvironment revealed by single-cell sequencing in head and neck cancer

Head and neck cancer (HNC) is the sixth most common disease in the world. The recurrence rate of patients is relatively high, and the heterogeneity of tumor immune microenvironment (TIME) cells may be an important reason for this. Single-cell sequencing (SCS) is currently the most promising and mature application in cancer research. It can identify unique genes expressed in cells and study tumor heterogeneity. According to current research, the heterogeneity of immune cells has become an important factor affecting the occurrence and development of HNC. SCSs can provide effective therapeutic targets and prognostic factors for HNC patients through analyses of gene expression levels and cell heterogeneity. Therefore, this study analyzes the basic theory of HNC and the development of SCS technology, elaborating on the application of SCS technology in HNC and its potential value in identifying HNC therapeutic targets and biomarkers.

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 more than 70% of cases. In primary but not metastatic melanoma biopsies, the burden of coding mutations in desmosome genes is associated with a strong reduction in desmosome gene expression. Analysis by spatial transcriptomics and protein immunofluorescence suggests that these decreases in expression occur in keratinocytes in the microenvironment rather than in primary melanoma cells. In further support of a microenvironmental origin, we find that desmosome gene knockdown in keratinocytes yields markedly increased proliferation of adjacent melanoma cells in keratinocyte and melanoma cocultures. Similar increases in melanoma proliferation are observed in media preconditioned with desmosome-deficient keratinocytes. Thus, gradual accumulation of desmosome mutations in neighboring cells may prime melanoma cells for neoplastic transformation.

Systematic evaluation of single-cell RNA-seq analyses performance based on long-read sequencing platforms

INTRODUCTION

The rapid development of next-generation sequencing (NGS)-based single-cell RNA sequencing (scRNA-seq) allows for detecting and quantifying gene expression in a high-throughput manner, providing a powerful tool for comprehensively understanding cellular function in various biological processes. However, the NGS-based scRNA-seq only quantifies gene expression and cannot reveal the exact transcript structures (isoforms) of each gene due to the limited read length. On the other hand, the long read length of third-generation sequencing (TGS) technologies, including Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), enable direct reading of intact cDNA molecules.

OBJECTIVES

Both ONT and PacBio have been used in conjunction with scRNA-seq, but their performance in single-cell analyses has not been systematically evaluated.

METHODS

To address this, we generated ONT and PacBio data from the same single-cell cDNA libraries containing different amount of cells.

RESULTS

Using NGS as a control, we assessed the performance of each platform in cell type identification. Additionally, the reliability in identifying novel isoforms and allele-specific gene/isoform expression by both platforms was verified, providing a systematic evaluation to design the sequencing strategies in single-cell transcriptome studies.

CONCLUSION

Beyond gene expression analysis, which the NGS-based scRNA-seq only affords, TGS-based scRNA-seq achieved gene splicing analyses, identifying novel isoforms. Attribute to higher sequencing quality of PacBio, it outperforms ONT in accuracy of novel transcripts identification and allele-specific gene/isoform expression.

NAALAD2 mutations disrupt the fate of photoreceptor cells and retinal pigment epithelial cells during early retinal development

In recent years, the global incidence of myopia has steadily increased, highlighting the importance of prevention and early intervention, particularly in the absence of effective treatments. Here, we identified a previously unreported mutation in the human N-acetylated alpha-linked acidic dipeptidase 2 (NAALAD2) gene (c.2109 T > G, p.F703L) considered in a Chinese family with pathological myopia (PM). We explored the potential link between NAALAD2 mutation and the development of PM by using the Naalad2 point mutation knock-in mouse models. Through single-cell RNA sequencing, we analyzed the retinal cell composition and transcriptional profiles both in Naalad2+/+ and Naalad2+/- mice, especially the changes in Cone photoreceptor cells, Rod photoreceptor cells and retinal pigment epithelial (RPE) cells. We found that the Naalad2 mutation led to a reduction in the abundance of Cone and Rod photoreceptor cells, along with upregulation of immediate early genes and abnormal differentiation of certain cell subpopulations. Additionally, RPE cell subpopulations exhibited a fibrotic tendency, disrupting their interactions with photoreceptor cells. Moreover, this study suggests that NAALAD2 mutation may accelerate retinal degeneration by influencing photoreceptor cell apoptosis, stress responses, and the epithelial-mesenchymal transition process in RPE cells. These findings provide new insights into the pathogenic mechanisms of NAALAD2 mutations in PM and offer potential therapeutic targets for future PM research.

ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction

With the development of single-cell RNA-sequencing (scRNA-seq) technology, scRNA-seq data analysis suffers huge challenges due to large scale, high dimensionality, high noise, and high sparsity. To achieve accurately embedded representation in the large-scale scRNA-seq data, we try to design a novel graph convolutional network with an adaptive aggregation mechanism. Based on the assumption that the aggregation order of different cells would be different, a graph convolutional network with an adaptive aggregation-based dimensionality reduction algorithm for scRNA-seq data is developed, named scAGCN. In scAGCN, a preprocessing consisting of quality control and feature selection is implemented. Then, an approximate nearest neighbor graph is rapidly constructed. Finally, a graph convolutional network with an adaptive aggregation mechanism is constructed, in which the neighborhood selection strategy based on node distribution and similarity boxplots is designed, and the aggregation function is optimized by defining a similarity measurement between neighborhood nodes and the central node. The results show that scAGCN outperforms existing dimensionality reduction methods on 15 real scRNA-seq datasets, especially in 10 large-scale scRNA-seq datasets.

New Insights and Implications of Cell-Cell Interactions in Developmental Biology

The dynamic and meticulously regulated networks established the foundation for embryonic development, where the intercellular interactions and signal transduction assumed a pivotal role. In recent years, high-throughput technologies such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have advanced dramatically, empowering the systematic dissection of cell-to-cell regulatory networks. The emergence of comprehensive databases and analytical frameworks has further provided unprecedented insights into embryonic development and cell-cell interactions (CCIs). This paper reviewed the exponential increased CCIs works related to developmental biology from 2008 to 2023, comprehensively collected and categorized 93 analytical tools and 39 databases, and demonstrated its practical utility through illustrative case studies. In parallel, the article critically scrutinized the persistent challenges within this field, such as the intricacies of spatial localization and transmembrane state validation at single-cell resolution, and underscored the interpretative limitations inherent in current analytical frameworks. The development of CCIs' analysis tools with harmonizing multi-omics data and the construction of cross-species dynamically updated CCIs databases will be the main direction of future research. Future investigations into CCIs are poised to expeditiously drive the application and clinical translation within developmental biology, unlocking novel dimensions for exploration and progress.

Temporal and spatial omics technologies for 4D profiling

Cells have distinct molecular repertoires on their surfaces and unique intracellular biomolecular profiles that play pivotal roles in orchestrating a myriad of biological responses in the context of growth, development and disease. A persistent challenge in the deep exploration of these cues has been in our inability to effectively and precisely capture the temporal and spatial characteristics of living cells. In this Perspective, we delve into techniques for temporal and two- and three-dimensional spatial omics analyses and underscore how their harmonious fusion promises to unlock insights into the dynamics and diversity of individual cells within biological systems such as tissues and organoids. We then explore four-dimensional profiling, a nascent but promising frontier that adds a temporal (fourth-dimension) component to three-dimensional omics; highlight the advancements, challenges and gaps in the field; and discuss potential strategies for further technological development.

Heterogeneity-preserving discriminative feature selection for disease-specific subtype discovery

Disease-specific subtype identification can deepen our understanding of disease progression and pave the way for personalized therapies, given the complexity of disease heterogeneity. Large-scale transcriptomic, proteomic, and imaging datasets create opportunities for discovering subtypes but also pose challenges due to their high dimensionality. To mitigate this, many feature selection methods focus on selecting features that distinguish known diseases or cell states, yet often miss features that preserve heterogeneity and reveal new subtypes. To overcome this gap, we develop Preserving Heterogeneity (PHet), a statistical methodology that employs iterative subsampling and differential analysis of interquartile range, in conjunction with Fisher's method, to identify a small set of features that enhance subtype clustering quality. Here, we show that this method can maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods, indicating its potential to advance our understanding of disease mechanisms and cell differentiation.

Diagnosing recipient- vs. donor-derived posttransplant myelodysplastic neoplasm via targeted single-cell mutational profiling

BACKGROUND

Distinguishing donor- vs. recipient-derived myelodysplastic neoplasm (MDS) after allogeneic hematopoietic stem cell transplantation (allo-HSCT) is challenging and has direct therapeutical implications.

METHODS

Here, we took a translational approach that we used in addition to conventional diagnostic techniques to resolve the origin of MDS in a 38-year-old patient with acquired aplastic anemia and evolving MDS after first allo-HSCT. Specifically, we used single-cell transcriptional profiling to differentiate between donor- and recipient-derived bone marrow cells and established a strategy that additionally allows identification of cells carrying the MDS-associated U2AF1S34Y variant.

RESULTS

The patient exhibited mixed donor chimerism combined with severely reduced erythropoiesis and dysplastic morphology within the granulocytic and megakaryocytic lineage along with the MDS-associated U2AF1S34Y mutation in the bone marrow. Single-cell transcriptional profiling together with targeted enrichment of the U2AF1S34Y-specific locus further revealed that, while the immune compartment was mainly populated by donor-derived cells, myelopoiesis was predominantly driven by the recipient. Additionally, concordant with recipient-derived MDS, we found that U2AF1S34Y-mutated cells were exclusively recipient derived with X but not Y chromosome-specific gene expression.

CONCLUSION

Our study highlights the clinical potential of integrating high-resolution single-cell techniques to resolve complex cases for personalized treatment decisions.

FUNDING

The study was funded by intramural resources of the Charité - Universitätsmedizin Berlin and the Berlin Institute of Health.

Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data

Bursty gene expression is characterized by two intuitive parameters, burst frequency and burst size, the cell-cycle dependence of which has not been extensively profiled at the transcriptome level. In this study, we estimate the burst parameters per allele in the G1 and G2/M cell-cycle phases for thousands of mouse genes by fitting mechanistic models of gene expression to messenger RNA count data, obtained by sequencing of single cells whose cell-cycle position has been inferred using a deep-learning method. We find that upon DNA replication, the median burst frequency approximately halves, while the burst size remains mostly unchanged. Genome-wide distributions of the burst parameter ratios between the G2/M and G1 phases are broad, indicating substantial heterogeneity in transcriptional regulation. We also observe a significant negative correlation between the burst frequency and size ratios, suggesting that regulatory processes do not independently control the burst parameters. We show that to accurately estimate the burst parameter ratios, mechanistic models must explicitly account for gene copy number variation and extrinsic noise due to the coupling of transcription to cell age across the cell cycle, but corrections for technical noise due to imperfect capture of RNA molecules in sequencing experiments are less critical.

scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data

BACKGROUND

Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First, they do not fully exploit cellular features. Second, they are developed based on gene expression information and lack of flexibility in integrating intercellular relationships. Finally, the performance of these methods is affected by dropout event.

RESULTS

We propose a novel deep learning (DL) model based on attention autoencoder and zero-inflated (ZI) layer, namely scAMZI, to cluster scRNA-seq data. scAMZI is mainly composed of SimAM (a Simple, parameter-free Attention Module), autoencoder, ZINB (Zero-Inflated Negative Binomial) model and ZI layer. Based on ZINB model, we introduce autoencoder and SimAM to reduce dimensionality of data and learn feature representations of cells and relationships between cells. Meanwhile, ZI layer is used to handle zero values in the data. We compare the performance of scAMZI with nine methods (three shallow learning algorithms and six state-of-the-art DL-based methods) on fourteen benchmark scRNA-seq datasets of various sizes (from hundreds to tens of thousands of cells) with known cell types. Experimental results demonstrate that scAMZI outperforms competing methods.

CONCLUSIONS

scAMZI outperforms competing methods and can facilitate downstream analyses such as cell annotation, marker gene discovery, and cell trajectory inference. The package of scAMZI is made freely available at https://doi.org/10.5281/zenodo.13131559 .

A realistic FastQ-based framework FastQDesign for ScRNA-seq study design issues

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technology for characterizing transcriptomic profiles at single-cell resolution. It is crucial to consider both the number of cells and sequencing depth during library preparation. The existing methods are primarily simulation-based, rely on Unique Molecular Identifier (UMI) matrix, and have little context in the actual FastQ reads. Here we propose the first FastQ-based study design framework, named "FastQDesign," which leverages raw FastQ files from publicly available datasets as references and suggests an optimal design within a fixed budget. We demonstrate our framework through a synthetic dataset and applications to nine real-world datasets. Our study underscores the importance of an appropriate design to investigate the biology of heterogeneous cell populations and offers practical guidance considering cost-benefit trade-offs. A high-efficiency software suite is available at https://github.com/yuw444/FastQDesign .

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.

Cancer-associated fibroblasts in breast cancer in the single-cell era: Opportunities and challenges

Breast cancer is a leading cause of morbidity and mortality in women, and its progression is closely linked to the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), key components of the TME, play a crucial role in promoting tumor growth by driving cancer cell proliferation, invasion, extracellular matrix (ECM) remodeling, inflammation, chemoresistance, and immunosuppression. CAFs exhibit considerable heterogeneity and are classified into subgroups based on different combinations of biomarkers. Single-cell RNA sequencing (scRNA-seq) enables high-throughput and high-resolution analysis of individual cells. Relying on this technology, it is possible to cluster complex CAFs according to different biomarkers to analyze the specific phenotypes and functions of different subpopulations. This review explores CAF clusters in breast cancer and their associated biomarkers, highlighting their roles in disease progression and potential for targeted therapies.

Large-scale combinatorial optical barcoding of cells with laser particles

The identification of individual cells is crucial for advancements in single-cell analysis. Optically readable barcodes provide a means to distinguish and track cells through repeated, non-destructive measurements. Traditional fluorophore-based methods are limited by the finite number of unique barcodes they can produce. Laser particles (LPs), which emit narrowband peaks over a wide spectral range, have emerged as a promising technology for single-cell barcoding. Here, we demonstrate the use of multiple LPs to generate combinatorial barcodes, enabling the identification of a vast number of live cells. We introduce a theoretical framework for estimating the number of LPs required for unique barcodes and the expected identification error rate. Additionally, we present an improved LP-tagging method that is highly effective across a variety of cell types and evaluate its biocompatibility. Our experimental results show successful barcoding of several million cells, closely matching our theoretical predictions. This research marks a significant step forward in the scalability of LP technology for single-cell tracking and analysis.

How to view the female reproductive tract through single-cell looking glasses

Single-cell technologies have emerged as an unprecedented tool for biologists and clinicians, allowing them to assess organs and tissues at the level of individual cells. In the field of women's reproductive biology, single-cell studies have provided insights into the cellular and molecular processes that regulate reproductive and obstetrical functions in health and disease. The knowledge that these studies generate is helping clinicians to improve the understanding and diagnosis of infertility related issues or pregnancy complications and to find new avenues for their treatment. However, navigating the expansive landscape of this type of transcriptomic data analysis represents a pivotal challenge in current research. Single cell RNA sequencing involves isolating cells into droplets, reverse transcribing RNA to generate complementary DNA, with each droplet content uniquely labeled by a barcode. Upon sequencing the complementary DNAs, the barcodes enable the reassignment of sequencing reads to individual droplets, facilitating the reconstruction of the cellular landscape of the sample obtained from a tissue or organ and beyond. Researchers, equipped with the metaphorical 'single-cell glasses,' must adequately choose from a plethora of strategies to dissect and interpret cellular information. Sophisticated algorithms and the decision-making process are often underestimated, resulting in artefactual or cumbersome interpreted results. Computational biologists apply and innovate computational tools designed to process, model, and interpret expansive datasets. The ramifications of their work extend far beyond the realm of data processing; they give shape to the outcome of analyses, playing a pivotal role in drawing meaningful conclusions from the wealth of information garnered. In this review, we describe the wide variety of approaches and analytical steps available with enough detail to gain a concise picture of what a complete examination of a single-cell dataset would be. We commence with a discussion on key points in experimental design, highlighting crucial questions one should consider. Following this, we delve into the various preprocessing and quality control steps essential for any single-cell dataset. The subsequent section offers a detailed guide on constructing a single-cell atlas, exploring nuances such as differential characteristics in visualization and clustering techniques, as well as strategies for assigning identity to cell populations through gene marker annotations. Moving beyond the creation of an atlas, we explore methods for investigating pathological conditions. This involves conducting cell population comparison tests between conditions and analyzing specific cell-to-cell communications and cellular differentiation trajectories in both health and disease scenarios. This work aims to furnish a newcomer researcher and/or clinician with essential guidelines to embark on a single-cell adventure without succumbing to common pitfalls. By bridging the gap between theory and practice, it facilitates the translation of single-cell technologies into clinically relevant applications. Throughout the manuscript, practical examples of its usage in women's reproductive health studies are provided. Various sections delve into specific clinical scenarios, demonstrating how these guidelines can be instrumental in unraveling the molecular landscapes of diseases and physiological processes related to women's reproduction.

Stem cell culture conditions affect in vitro differentiation potential and mouse gastruloid formation

Aggregating low numbers of mouse embryonic stem cells (mESCs) and inducing Wnt signalling generates 'gastruloids', self-organising complex structures that display an anteroposterior organisation of cell types derived from all three germ layers. Current gastruloid protocols display considerable heterogeneity between experiments in terms of morphology, elongation efficiency, and cell type composition. We therefore investigated whether altering the mESC pluripotency state would provide more consistent results. By growing three mESC lines from two different genetic backgrounds in different intervals of ESLIF and 2i medium the pluripotency state of cells was modulated, and mESC culture as well as the resulting gastruloids were analysed. Microscopic analysis showed a pre-culture-specific effect on gastruloid formation, in terms of aspect ratio and reproducibility. RNA-seq analysis of the mESC start population confirmed that short-term pulses of 2i and ESLIF modulate the pluripotency state, and result in different cellular states. Since multiple epigenetic regulators were detected among the top differentially expressed genes, we further analysed genome-wide DNA methylation and H3K27me3 distributions. We observed epigenetic differences between conditions, most dominantly in the promoter regions of developmental regulators. Lastly, when we investigated the cell type composition of gastruloids grown from these different pre-cultures, we observed that mESCs subjected to 2i-ESLIF preceding aggregation generated gastruloids more consistently, including more complex mesodermal contributions as compared to the ESLIF-only control. These results indicate that optimisation of the mESCs pluripotency state allows the modulation of cell differentiation during gastruloid formation.

Pinning-Induced Microdroplet Self-Transport

Droplets are prone to adhere or "pin" on solid surfaces which contain unavoidable micro- and nanoscale surface defects formed through chemical and topographical heterogeneity. To initiate droplet motion, potential energy gradients, surface energy gradients, or external energy input are needed. Here, in contrast to established wisdom, we show that properly designed surface heterogeneity can promote microdroplet self-transport without any external force or anisotropy. In the presence of topological defects, microdroplets can take advantage of contact line pinning to generate contact line and corresponding contact angle asymmetry, leading to spontaneous motion over distances 10-20 times larger than the droplet radius. The outcomes of this work present an alternative pathway for taking advantage of intrinsic surface heterogeneity to achieve droplet mobility in a range of applications, where passive droplet motion is desired.

Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

What features of transcription can be learnt by fitting mathematical models of gene expression to mRNA count data? Given a suite of models, fitting to data selects an optimal one, thus identifying a probable transcriptional mechanism. Whilst attractive, the utility of this methodology remains unclear. Here, we sample steady-state, single-cell mRNA count distributions from parameters in the physiological range, and show they cannot be used to confidently estimate the number of inactive gene states, i.e. the number of rate-limiting steps in transcriptional initiation. Distributions from over 99% of the parameter space generated using models with 2, 3, or 4 inactive states can be well fit by one with a single inactive state. However, we show that for many minutes following induction, eukaryotic cells show an increase in the mean mRNA count that obeys a power law whose exponent equals the sum of the number of states visited from the initial inactive to the active state and the number of rate-limiting post-transcriptional processing steps. Our study shows that estimation of the exponent from eukaryotic data can be sufficient to determine a lower bound on the total number of regulatory steps in transcription initiation, splicing, and nuclear export.

Protocol for the isolation of silk glands from silkworms for snRNA-seq and spatial transcriptomics

The silk glands (SGs) of silkworms specifically synthesize silk proteins, thus strongly influencing the yield and quality of silk. Here, we present a protocol for isolating SG nuclei from silkworms and obtaining high-quality tissue slices for spatial transcriptomics. We describe steps for rearing, dissecting, and nucleus isolation. We then detail procedures for embedding, frozen section, and RNA capturing and sequencing. This protocol enables the exploration of the spatial distribution of SG cells at single-cell resolution. For complete details on the use and execution of this protocol, please refer to Ma et al.1.

Population dynamics modeling reveals that myeloid bias involves both HSC differentiation and progenitor proliferation biases

ABSTRACT

Aging and chronic inflammation are associated with overabundant myeloid-primed multipotent progenitors (MPPs) among hematopoietic stem and progenitor cells (HSPCs). Although hematopoietic stem cell (HSC) differentiation bias has been considered a primary cause of myeloid bias, whether it is sufficient has not been quantitatively evaluated. Here, we analyzed bone marrow data from the IκB- (Nfkbia+/-Nfkbib-/-Nfkbie-/-) mouse model of inflammation with elevated NFκB activity, which reveals increased myeloid-biased MPPs. We interpreted these data with differential equation models of population dynamics to identify alterations of HSPC proliferation and differentiation rates. This analysis revealed that short-term HSC differentiation bias alone is likely insufficient to account for the increase in myeloid-biased MPPs. To explore additional mechanisms, we used single-cell RNA sequencing (scRNA-seq) measurements of IκB- and wild-type HSPCs to track the continuous differentiation trajectories from HSCs to erythrocyte/megakaryocyte, myeloid, and lymphoid primed progenitors. Fitting a partial differential equations model of population dynamics to these data revealed not only less lymphoid-fate specification among HSCs but also increased expansion of early myeloid-primed progenitors. Differentially expressed genes along the differentiation trajectories supported increased proliferation among these progenitors. These findings were conserved when wild-type HSPCs were transplanted into IκB- recipients, indicating that an inflamed bone marrow microenvironment is a sufficient driver. We then applied our analysis pipeline to scRNA-seq measurements of HSPCs isolated from aged mice and human patients with myeloid neoplasms. These analyses identified the same myeloid-primed progenitor expansion as in the IκB- models, suggesting that it is a common feature across different settings of myeloid bias.

Spatial transcriptomics in the adult Drosophila brain and body

Recently, we have achieved a significant milestone with the creation of the Fly Cell Atlas. This single-nuclei atlas encompasses the entire fly, covering the entire head and body, in addition to all major organs. This atlas catalogs many hundreds of cell types, of which we annotated 250. Thus, a large number of clusters remain to be fully characterized, in particular in the brain. Furthermore, by applying single-nuclei sequencing, all information about the spatial location of the cells in the body and of about possible subcellular localization of the mRNAs within these cells is lost. Spatial transcriptomics promises to tackle these issues. In a proof-of-concept study, we have here applied spatial transcriptomics using a selected gene panel to pinpoint the locations of 150 mRNA species in the adult fly. This enabled us to map unknown clusters identified in the Fly Cell Atlas to their spatial locations in the fly brain. Additionally, spatial transcriptomics discovered interesting principles of mRNA localization and transcriptional diversity within the large and crowded muscle cells that may spark future mechanistic investigations. Furthermore, we present a set of computational tools that will allow for easier integration of spatial transcriptomics and single-cell datasets.

Multi-step genomics on single cells and live cultures in sub-nanoliter capsules

Single-cell genomics encompasses a set of methods whereby hundreds to millions of cells are individually subjected to multiplexed assays including sequencing DNA, chromatin accessibility or modification, RNA, or combinations thereof1,2. These methods enable unbiased, systematic discovery of cellular phenotypes and their dynamics1-3. Many functional genomic methods, however, require multiple steps that cannot be easily scaled to high throughput, including assays on living cells. Here we develop capsules with amphiphilic gel envelopes (CAGEs), which selectively retain cells, mRNA, and gDNA, while allowing free diffusion of media, enzymes and reagents. CAGEs enable carrying out high-throughput assays that require multiple steps, including combining genomics with live-cell assays. We establish methods for barcoding CAGE DNA and RNA libraries, and apply them to measure persistence of gene expression programs by capturing the transcriptomes of tens of thousands of expanding clones in CAGEs. The compatibility of CAGEs with diverse enzymatic reactions will facilitate the expansion of the current repertoire of single-cell, high-throughput measurements and extend them to live-cell assays.

High-throughput single cell -omics using semi-permeable capsules

Biological systems are inherently complex and heterogeneous. Deciphering this complexity increasingly relies on high-throughput analytical methods and tools that efficiently probe the cellular phenotype and genotype. While recent advancements have enabled various single-cell -omics assays, their broader applications are inherently limited by the challenge of efficiently conducting multi-step biochemical assays while retaining various biological analytes. Extending on our previous work (1) here we present a versatile technology based on semi-permeable capsules (SPCs), tailored for a variety of high-throughput nucleic acid assays, including digital PCR, genome sequencing, single-cell RNA-sequencing (scRNA-Seq) and FACS-based isolation of individual transcriptomes based on nucleic acid marker of interest. Being biocompatible, the SPCs support single-cell cultivation and clonal expansion over long periods of time - a fundamental limitation of droplet microfluidics systems. Using SPCs we perform scRNA-Seq on white blood cells from patients with hematopoietic disorders and demonstrate that capsule-based sequencing approach (CapSeq) offers superior transcript capture, even for the most challenging cell types. By applying CapSeq on acute myeloid leukemia (AML) samples, we uncover notable changes in transcriptomes of mature granulocytes and monocytes associated with blast and progenitor cell phenotypes. Accurate representation of the entirety of the cellular heterogeneity of clinical samples, driving new insights into the malfunctioning of the innate immune system, and ability to clonally expand individual cells over long periods of time, positions SPC technology as customizable, highly sensitive and broadly applicable tool for easy-to-use, scalable single-cell -omics applications.

scCCTR: An iterative selection-based semi-supervised clustering model for single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) enables the analysis of the genome, transcriptome, and epigenome at the single-cell level, providing a critical tool for understanding cellular heterogeneity and diversity. Cell clustering, a key step in scRNA-seq data analysis, reveals population structure by grouping cells with similar expression patterns. However, due to the high dimensionality and sparsity of scRNA-seq data, the performance of existing clustering algorithms remains suboptimal. In this study, we propose a novel clustering algorithm, scCCTR, which performs semi-supervised classification by guiding a deep learning model through iterative selection of high-confidence cells and labels. The algorithm consists of two main components: an iterative selection module and a semi-supervised classification module. In the iterative selection module, scCCTR progressively selects high-confidence cells that exhibit core group features and iteratively optimizes feature representations, constructing a consensus clustering result throughout the iterations. In the semi-supervised classification module, scCCTR uses the selected core data to train a Transformer neural network, which leverages a multi-head attention mechanism to focus on critical information, thereby achieving higher clustering precision. We compared scCCTR with several established cell clustering methods on real datasets, and the results demonstrate that scCCTR outperforms existing methods in terms of accuracy and effectiveness for both cell clustering and visualization. (The code of scCCTR is free available for academic https://github.com/chenjiejie387/scCCTR).

High-throughput, combinatorial droplet generation by sequential spraying

Advancements in bulk and microfluidic emulsion methodologies have enabled highly efficient, high-throughput implementations of biochemical assays. Spray-based techniques offer rapid generation, droplet immobilization, and accessibility, but remain relatively underutilized, likely because they result in random and polydisperse droplets. However, the polydisperse characteristic can be leveraged; at sufficiently high droplet numbers, sequential sprays will generate mixed droplets which effectively populate a combinatorial space. In this paper, we present a method involving the sequential spraying and mixing of solutions encoded with fluorophores. This generates combinatorial droplets with quantifiable concentrations that can be imaged over time. To demonstrate the method's performance and utility, we use it to investigate synergistic and antagonistic pairwise antibiotic interactions.

Exploring Single-Probe Single-Cell Mass Spectrometry: Current Trends and Future Directions

The Single-probe single-cell mass spectrometry (SCMS) is an innovative analytical technique designed for metabolomic profiling, offering a miniaturized, multifunctional device capable of direct coupling to mass spectrometers. It is an ambient technique leveraging microscale sampling and nanoelectrospray ionization (nanoESI), enabling the analysis of cells in their native environments without the need for extensive sample preparation. Due to its miniaturized design and versatility, this device allows for applications in diverse research areas, including single-cell metabolomics, quantification of target molecules in single cell, MS imaging (MSI) of tissue sections, and investigation of extracellular molecules in live single spheroids. This review explores recent advancements in Single-probe-based techniques and their applications, emphasizing their potential utility in advancing MS methodologies in microscale bioanalysis.

High-throughput single-microbe RNA sequencing reveals adaptive state heterogeneity and host-phage activity associations in human gut microbiome

Microbial communities such as those residing in the human gut are highly diverse and complex, and many with important implications for health and diseases. The effects and functions of these microbial communities are determined not only by their species compositions and diversities but also by the dynamic intra- and inter-cellular states at the transcriptional level. Powerful and scalable technologies capable of acquiring single-microbe-resolution RNA sequencing information in order to achieve a comprehensive understanding of complex microbial communities together with their hosts are therefore utterly needed. Here we report the development and utilization of a droplet-based smRNA-seq (single-microbe RNA sequencing) method capable of identifying large species varieties in human samples, which we name smRandom-seq2. Together with a triple-module computational pipeline designed for the bacteria and bacteriophage sequencing data by smRandom-seq2 in four human gut samples, we established a single-cell level bacterial transcriptional landscape of human gut microbiome, which included 29,742 single microbes and 329 unique species. Distinct adaptive response states among species in Prevotella and Roseburia genera and intrinsic adaptive strategy heterogeneity in Phascolarctobacterium succinatutens were uncovered. Additionally, we identified hundreds of novel host-phage transcriptional activity associations in the human gut microbiome. Our results indicated that smRandom-seq2 is a high-throughput and high-resolution smRNA-seq technique that is highly adaptable to complex microbial communities in real-world situations and promises new perspectives in the understanding of human microbiomes.

Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective

The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interactions in both cellular and spatial contexts. Despite this progress, the analysis of single-cell and spatial omics data remains challenging. First, single-cell sequencing data are high-dimensional and sparse, and are often contaminated by noise and uncertainty, obscuring the underlying biological signal. Second, these data often encompass multiple modalities, including gene expression, epigenetic modifications, metabolite levels, and spatial locations. Integrating these diverse data modalities is crucial for enhancing prediction accuracy and biological interpretability. Third, while the scale of single-cell sequencing has expanded to millions of cells, high-quality annotated datasets are still limited. Fourth, the complex correlations of biological tissues make it difficult to accurately reconstruct cellular states and spatial contexts. Traditional feature engineering approaches struggle with the complexity of biological networks, while deep learning, with its ability to handle high-dimensional data and automatically identify meaningful patterns, has shown great promise in overcoming these challenges. Besides systematically reviewing the strengths and weaknesses of advanced deep learning methods, we have curated 21 datasets from nine benchmarks to evaluate the performance of 58 computational methods. Our analysis reveals that model performance can vary significantly across different benchmark datasets and evaluation metrics, providing a useful perspective for selecting the most appropriate approach based on a specific application scenario. We highlight three key areas for future development, offering valuable insights into how deep learning can be effectively applied to transcriptomic data analysis in biological, medical, and clinical settings.

AJGM: joint learning of heterogeneous gene networks with adaptive graphical model

MOTIVATION

Inferring gene networks provides insights into biological pathways and functional relationships among genes. When gene expression samples exhibit heterogeneity, they may originate from unknown subtypes, prompting the utilization of mixture Gaussian graphical model (GGM) for simultaneous subclassification and gene network inference. However, this method overlooks the heterogeneity of network relationships across subtypes and does not sufficiently emphasize shared relationships. Additionally, GGM assumes data follows a multivariate Gaussian distribution, which is often not the case with zero-inflated scRNA-seq data.

RESULTS

We propose an Adaptive Joint Graphical Model (AJGM) for estimating multiple gene networks from single-cell or bulk data with unknown heterogeneity. In AJGM, an overall network is introduced to capture relationships shared by all samples. The model establishes connections between the subtype networks and the overall network through adaptive weights, enabling it to focus more effectively on gene relationships shared across all networks, thereby enhancing the accuracy of network estimation. On synthetic data, the proposed approach outperforms existing methods in terms of sample classification and network inference, particularly excelling in the identification of shared relationships. Applying this method to gene expression data from triple-negative breast cancer confirms known gene pathways and hub genes, while also revealing novel biological insights.

AVAILABILITY AND IMPLEMENTATION

The Python code and demonstrations of the proposed approaches are available at https://github.com/yyytim/AJGM, and the software is archived in Zenodo with DOI: 10.5281/zenodo.14740972.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

Decoding the blueprints of embryo development with single-cell and spatial omics

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

Thermally Triggered Double Emulsion-Integrated Hydrogel Microparticles for Multiplexed Molecular Diagnostics

During the COVID-19 pandemic, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) has been recognized as the most reliable diagnostic tool. However, there is a need to develop multiplexed assays capable of analyzing multiple genes simultaneously to expand its application. To address this, a multiplexed RT-qPCR using a double emulsion (DE)-based carrier and a polymer microparticle reactor, termed primer-incorporated network tailored with Taqman probe (TaqPIN) is developed. The DE securely stores nucleic acid reagents like primers and probes within the polymer network until heating releases them for the reaction. The TaqPIN RT-qPCR demonstrates an amplification efficiency of 93.8% and can detect as few as 20 copies/µL. By loading the multiple microparticles into a single reaction, a multiplexed assay with only one optical channel is enabled. In practice, a nine-plex assay is designed to distinguish between variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Even subtle variations of a single nucleotide can be simultaneously detected. Testing on 75 nasopharyngeal swab samples yields 100% sensitivity and specificity for SARS-CoV-2 detection and 94% accuracy in variant discrimination.