Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues

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.

Exploring the untapped potential of single-cell and spatial omics in plant biology

Advances in single-cell and spatial omics technologies have revolutionised biology by revealing the diverse molecular states of individual cells and their spatial organization within tissues. The field of plant biology has widely adopted single-cell transcriptome and chromatin accessibility profiling and spatial transcriptomics, which extend traditional cell biology and genomics analyses and provide unique opportunities to reveal molecular and cellular dynamics of tissues. Using these technologies, comprehensive cell atlases have been generated in several model plant species, providing valuable platforms for discovery and tool development. Other emerging technologies related to single-cell and spatial omics, such as multiomics, lineage tracing, molecular recording, and high-content genetic and chemical perturbation phenotyping, offer immense potential for deepening our understanding of plant biology yet remain underutilised due to unique technical challenges and resource availability. Overcoming plant-specific barriers, such as cell wall complexity and limited antibody resources, alongside community-driven efforts in developing more complete reference atlases and computational tools, will accelerate progress. The synergy between technological innovation and targeted biological questions is poised to drive significant discoveries, advancing plant science. This review highlights the current applications of single-cell and spatial omics technologies in plant research and introduces emerging approaches with the potential to transform the field.

A large-scale integrated transcriptomic atlas for soybean organ development

Soybean is one of the most important crops globally, and its production must be significantly increased to meet increasing demand. Elucidating the genetic regulatory networks underlying soybean organ development is essential for breeding elite and resilient varieties to ensure increased soybean production under climate change. An integrated transcriptomic atlas that leverages multiple types of transcriptomics data can facilitate the characterization of temporal-spatial expression patterns of most organ development-related genes and thereby help us to understand organ developmental processes. Here, we constructed a comprehensive, integrated transcriptomic atlas for soybeans, integrating bulk RNA sequencing (RNA-seq) datasets from 314 samples across the soybean life cycle, along with single-nucleus RNA-seq and spatially enhanced resolution omics sequencing datasets from five organs: root, nodule, shoot apex, leaf, and stem. Investigating genes related to organ specificity, blade development, and nodule formation, we demonstrate that the atlas has robust power for exploring key genes involved in organ formation. In addition, we developed a user-friendly panoramic database for the transcriptomic atlas, enabling easy access and queries, which will serve as a valuable resource to significantly advance future soybean functional studies.

The dynamic and diverse nature of parenchyma cells in the Arabidopsis root during secondary growth

During secondary growth, the vascular cambium produces conductive xylem and phloem cells, while the phellogen (cork cambium) deposits phellem (cork) as the outermost protective barrier. Although most of the secondary tissues are made up of parenchyma cells, which are also produced by both cambia, their diversity and function are poorly understood. Here we combined single-cell RNA sequencing analysis with lineage tracing to recreate developmental trajectories of the cell types in the Arabidopsis root undergoing secondary growth. By analysing 93 reporter lines, we were able to identify 20 different cell types or cell states, many of which have not been described before. We additionally observed distinct transcriptome signatures of parenchyma cells depending on their maturation state and proximity to the conductive cell types. Our data show that both xylem and phloem parenchyma tissues are required for normal formation of conductive tissue cell types. Furthermore, we show that mature phloem parenchyma gradually obtains periderm identity, and this transformation can be accelerated by jasmonate treatment or wounding. Our study thus reveals the diversity of parenchyma cells and their capacity to undergo considerable identity changes during secondary growth.

Stepwise single-cell data identifies RNA binding proteins associated with the development of head and neck cancer and tumor microenvironment remodeling

Background: Head and neck squamous cell carcinoma (HNSC) is a globally prevalent malignancy with high mortality rates. RNA-binding proteins (RBPs) are crucial regulators of gene expression and play significant roles in cancer development. However, a comprehensive understanding of RBPs at the single-cell level in HNSC remains limited.ObjectiveThis study aims to investigate the role of RBPs in the stepwise progression of HNSC at the single-cell level, focusing on their expression patterns, prognostic potential, and involvement in key signaling pathways.MethodsWe analyzed single-cell RNA-sequencing data from HNSC samples across four stages, from normal tissue to precancerous leukoplakia, then to primary cancer and finally to metastatic tumors, examining the expression of 2141 previously reported RBPs. We identified RBP-based cell clusters and explored their associations with disease stages, cell types, and cancer progression. A prognostic risk model was developed based on RBPs with significant relevance to patient outcomes.ResultsRBPs displayed distinct cell type-specific expression patterns across different stages of HNSC. We found a significant correlation between RBP-based cell clusters and cancer progression. Notably, a prognostic model was constructed using RBPs such as CELF2, which showed downregulation from early leukoplakia to advanced cancer stages. Fibroblast RBPs were dynamically regulated, particularly in extracellular matrix remodeling, with key proteins like CFL1 and PFN1 linked to improved prognosis. Furthermore, we identified heterogeneity in RBP regulation of the Macrophage Migration Inhibitory Factor (MIF) signaling pathway across cell types during the precancerous stage.ConclusionsOur findings highlight the crucial roles of RBPs in HNSC progression and suggest their potential as therapeutic targets and prognostic markers, offering insights into personalized treatment strategies.

Single-cell RNA sequencing reveals heterogeneity and differential expression of the maternal-fetal interface during ICP and normal pregnancy

OBJECTIVE

Intrahepatic cholestasis of pregnancy (ICP) can cause adverse perinatal outcomes. Previous studies have demonstrated that the placenta of an ICP pregnancy differs in morphology and gene expression from the placenta of a normal pregnancy. To date, however, the genetic mechanism by which ICP affects the placenta is poorly understood. Therefore, the aim of this study was to investigate the differences in main cell types, gene signatures, cell ratio, and functional changes in the placenta between ICP and normal pregnancy.

METHODS

Single-cell RNA sequencing (scRNA-seq) technology was used to detect the gene expression of all cells at the placental maternal-fetal interface. Two individuals were analyzed - one with ICP and one without ICP. The classification of cell types was determined by a graph-based clustering algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the R software phyper () function and DAVID website. The differentially expressed genes (DEGs) encoding transcription factors (TFs) were identified using getorf and DIAMOND software.

RESULTS

We identified 14 cell types and 22 distinct cell subtypes that showed unique functional properties. Additionally, we found differences in the proportions of fibroblasts 1, helper T (Th) cells, extravillous trophoblasts, and villous cytotrophoblasts, and we observed heterogeneity of gene expression between ICP and control placentas. Furthermore, we identified 263 DEGs that belonged to TF families, including zf-C2H2, HMGI/HMGY, and Homeobox. In addition, 28 imprinted genes were preferentially expressed in specific cell types, such as PEG3 and PEG10 in trophoblasts as well as DLK1 and DIO3 in fibroblasts.

CONCLUSIONS

Our results revealed the differences in cell-type ratios, gene expression, and functional changes between ICP and normal placentas, and heterogeneity was found among cell subgroups. Hence, the imbalance of various cell types affects placental activity to varying degrees, indicating the complexity of the cell networks that form the placental tissue system, and this alteration of placental function is associated with adverse events in the perinatal period.

RNA-binding protein AZGP1 inhibits epithelial cell proliferation by regulating the genes of alternative splicing in COPD

BACKGROUND

Chronic Obstructive Pulmonary Disease (COPD) is characterized by high morbidity, disability, and mortality rates worldwide. RNA-binding proteins (RBPs) might regulate genes involved in oxidative stress and inflammation in COPD patients. Single-cell transcriptome sequencing (scRNA-seq) offers an accurate tool for identifying intercellular heterogeneity and the diversity of immune cells. However, the role of RBPs in the regulation of various cells, especially AT2 cells, remains elusive.

MATERIALS AND METHODS

A scRNA-seq dataset (GSE173896) and a bulk RNA-seq dataset acquired from airway tissues (GSE124180) were employed for data mining. Next, RNA-seq analysis was performed in both COPD and control patients. Differentially expressed genes (DEGs) were identified using criteria of fold change (FC ≥ 1.5 or ≤ 1.5) and P value ≤ 0.05. Lastly, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and alternative splicing identification analyses were carried out.

RESULTS

RBP genes exhibited specific expression patterns across different cell groups and participated in cell proliferation and mitochondrial dysfunction in AT2 cells. As an RBP, AZGP1 expression was upregulated in both the scRNA-seq and RNA-seq datasets. It might potentially be a candidate immune biomarker that regulates COPD progression by modulating AT2 cell proliferation and adhesion by regulating the expression of SAMD5, DNER, DPYSL3, GBP5, GBP3, and KCNJ2. Moreover, AZGP1 regulated alternative splicing events in COPD, particularly DDAH1 and SFRP1, holding significant implications in COPD.

CONCLUSION

RBP gene AZGP1 inhibits epithelial cell proliferation by regulating genes participating in alternative splicing in COPD.

An atlas of Brachypodium distachyon lateral root development

The root system of plants is a vital part for successful development and adaptation to different soil types and environments. A major determinant of the shape of a plant root system is the formation of lateral roots, allowing for expansion of the root system. Arabidopsis thaliana, with its simple root anatomy, has been extensively studied to reveal the genetic program underlying root branching. However, to get a more general understanding of lateral root development, comparative studies in species with a more complex root anatomy are required. Here, by combining optimized clearing methods and histology, we describe an atlas of lateral root development in Brachypodium distachyon, a wild, temperate grass species. We show that lateral roots initiate from enlarged phloem pole pericycle cells and that the overlying endodermis reactivates its cell cycle and eventually forms the root cap. In addition, auxin signaling reported by the DR5 reporter was not detected in the phloem pole pericycle cells or young primordia. In contrast, auxin signaling was activated in the overlying cortical cell layers, including the exodermis. Thus, Brachypodium is a valuable model to investigate how signaling pathways and cellular responses have been repurposed to facilitate lateral root organogenesis.

Optimal sequencing budget allocation for trajectory reconstruction of single cells

BACKGROUND

Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations.

RESULTS

Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.

Single-cell transcriptome atlas reveals somatic cell embryogenic differentiation features during regeneration

Understanding somatic cell totipotency remains a challenge facing scientific inquiry today. Plants display remarkable cell totipotency expression, illustrated by single-cell differentiation during somatic embryogenesis (SE) for plant regeneration. Determining cell identity and exploring gene regulation in such complex heterogeneous somatic cell differentiation have been major challenges. Here, we performed high-throughput single-cell sequencing assays to define the precise cellular landscape and revealed the modulation mode of marker genes during embryogenic differentiation in cotton (Gossypium hirsutum L.) as the crop for biotechnology application. We demonstrated that nonembryogenic calli (NEC) and primary embryogenic calli (PEC) tissues were composed of heterogeneous cells that could be partitioned into four broad populations with six distinct cell clusters. Enriched cell clusters and cell states were identified in NEC and PEC samples, respectively. Moreover, a broad repertoire of new cluster-specific genes and associated expression modules were identified. The energy metabolism, signal transduction, environmental adaptation, membrane transport pathways, and a series of transcription factors were preferentially enriched in cell embryogenic totipotency expression. Notably, the SE-ASSOCIATED LIPID TRANSFER PROTEIN (SELTP) gene dose-dependently marked cell types with distinct embryogenic states and exhibited a parabolic curve pattern along the somatic cell embryogenic differentiation trajectory, suggesting that SELTP could serve as a favorable quantitative cellular marker for detecting embryogenic expression at the single-cell level. In addition, RNA velocity and Scissor analysis confirmed the pseudo-temporal model and validated the accuracy of the scRNA-seq data, respectively. This work provides valuable marker-genes resources and defines precise cellular taxonomy and trajectory atlases for somatic cell embryogenic differentiation in plant regeneration.

Single-cell transcriptome revealed dysregulated RNA-binding protein expression patterns and functions in human ankylosing spondylitis

Objective

To explore the expression characteristics and regulatory patterns of RBPs in different immune cell types of AS, and to clarify the potential key role of RBPs in the occurrence and development of AS disease.

Methods

PBMC sample data from scRNA-seq (HC*29, AS*10) and bulk RNA-seq (NC*3, AS*5) were selected for correlation analysis.

Results

(1) Compared with the HC group, the numbers of B, DC (dendritic cells), CD14+ Mono and CD8+ T cells were increased in AS group, while the numbers of platelet (platelets), CD8+ NKT, CD16+ Mono (non-classical monocytes), Native CD4+ T and NK were decreased. (2) Through the analysis of RBP genes in B cells, some RBPs were found to play an important role in B cell differentiation and function, such as DDX3X, SFPQ, SRRM1, UPF2. (3) It may be related to B-cell receptor, IgA immunity, NOD-like receptor and other signaling pathways; Through the analysis of RBP genes in CD8+ T cells, some RBPs that play an important role in the immune regulation of CD8+ T were found, such as EIF2S3, EIF4B, HSPA5, MSL3, PABPC1 and SRSF7; It may be related to T cell receptor, TNF, IL17 and other signaling pathways. (4) Based on bulk RNA-seq, it was found that compared with HC and AS patients, differentially expressed variable splicing genes (RASGs) may play an important role in the occurrence and development of AS by participating in transcriptional regulation, protein phosphorylation and ubiquitination, DNA replication, angiogenesis, intracellular signal transduction and other related pathways.

Conclusion

RBPs has specific expression characteristics in different immune cell types of AS patients, and has important regulatory functions. Its abnormal expression and regulation may be closely related to the occurrence and development of AS.

Single-cell transcriptomics analysis of cellular heterogeneity and immune mechanisms in neurodegenerative diseases

Single-cell transcriptomics analysis is an advanced technology that can describe the intracellular transcriptome in complex tissues. It profiles and analyses datasets by single-cell RNA sequencing. Neurodegenerative diseases are identified by the abnormal apoptosis of neurons in the brain with few or no effective therapy strategies at present, which has been a growing healthcare concern and brought a great burden to society. The transcriptome of individual cells provides deep insights into previously unforeseen cellular heterogeneity and gene expression differences in neurodegenerative disorders. It detects multiple cell subsets and functional changes during pathological progression, which deepens the understanding of the molecular underpinnings and cellular basis of neurodegenerative diseases. Furthermore, the transcriptome analysis of immune cells shows the regulation of immune response. Different subtypes of immune cells and their interaction are found to contribute to disease progression. This finding enables the discovery of novel targets and biomarkers for early diagnosis. In this review, we emphasize the principles of the technology, and its recent progress in the study of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. The application of single-cell transcriptomics analysis in neurodegenerative disorders would help explore the pathogenesis of these diseases and develop novel therapeutic methods.

Emerging applications of single-cell profiling in precision medicine of atherosclerosis

Atherosclerosis is a chronic, progressive, inflammatory disease that occurs in the arterial wall. Despite recent advancements in treatment aimed at improving efficacy and prolonging survival, atherosclerosis remains largely incurable. In this review, we discuss emerging single-cell sequencing techniques and their novel insights into atherosclerosis. We provide examples of single-cell profiling studies that reveal phenotypic characteristics of atherosclerosis plaques, blood, liver, and the intestinal tract. Additionally, we highlight the potential clinical applications of single-cell analysis and propose that combining this approach with other techniques can facilitate early diagnosis and treatment, leading to more accurate medical interventions.

scRNA-seq reveals that origin recognition complex subunit 6 regulates mouse spermatogonial cell proliferation and apoptosis via activation of Wnt/β-catenin signaling

The regulation of spermatogonial proliferation and apoptosis is of great significance for maintaining spermatogenesis. The single-cell RNA sequencing (scRNA-seq) analysis of the testis was performed to identify genes upregulated in spermatogonia. Using scRNA-seq analysis, we identified the spermatogonia upregulated gene origin recognition complex subunit 6 ( Orc6 ), which is involved in DNA replication and cell cycle regulation; its protein expression in the human and mouse testis was detected by western blot and immunofluorescence. To explore the potential function of Orc6 in spermatogonia, the C18-4 cell line was transfected with control or Orc6 siRNA. Subsequently, 5-ethynyl-2-deoxyuridine (EdU) and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assays, flow cytometry, and western blot were used to evaluate its effects on proliferation and apoptosis. It was revealed that ORC6 could promote proliferation and inhibit apoptosis of C18-4 cells. Bulk RNA sequencing and bioinformatics analysis indicated that Orc6 was involved in the activation of wingless/integrated (Wnt)/β-catenin signaling. Western blot revealed that the expression of β-catenin protein and its phosphorylation (Ser675) were significantly decreased when silencing the expression of ORC6. Our findings indicated that Orc6 was upregulated in spermatogonia, whereby it regulated proliferation and apoptosis by activating Wnt/β-catenin signaling.

Peripheral T cell activation, not thymic selection, expands the T follicular helper repertoire in a lupus-prone murine model

Many autoimmune diseases are characterized by the activation of autoreactive T cells. The T cell repertoire is established in the thymus; it remains uncertain whether the presence of disease-associated autoreactive T cells reflects abnormal T cell selection in the thymus or aberrant T cell activation in the periphery. Here, we describe T cell selection, activation, and T cell repertoire diversity in female mice deficient for B lymphocyte-induced maturation protein (BLIMP)-1 in dendritic cells (DCs) (Prdm1 CKO). These mice exhibit a lupus-like phenotype with an expanded population of T follicular helper (Tfh) cells having a more diverse T cell receptor (TCR) repertoire than wild-type mice and, in turn, develop a lupus-like pathology. To understand the origin of the aberrant Tfh population, we analyzed the TCR repertoire of thymocytes and naive CD4 T cells from Prdm1 CKO mice. We show that early development and selection of T cells in the thymus are not affected. Importantly, however, we observed increased TCR signal strength and increased proliferation of naive T cells cultured in vitro with antigen and BLIMP1-deficient DCs compared to control DCs. Moreover, there was increased diversity in the TCR repertoire in naive CD4+ T cells stimulated in vitro with BLIMP1-deficient DCs. Collectively, our data indicate that lowering the threshold for peripheral T cell activation without altering thymic selection and naive T cell TCR repertoire leads to an expanded repertoire of antigen-activated T cells and impairs peripheral T cell tolerance.

Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids

Due to the high dimensionality and sparsity of the gene expression matrix in single-cell RNA-sequencing (scRNA-seq) data, coupled with significant noise generated by shallow sequencing, it poses a great challenge for cell clustering methods. While numerous computational methods have been proposed, the majority of existing approaches center on processing the target dataset itself. This approach disregards the wealth of knowledge present within other species and batches of scRNA-seq data. In light of this, our paper proposes a novel method named graph-based deep embedding clustering (GDEC) that leverages transfer learning across species and batches. GDEC integrates graph convolutional networks, effectively overcoming the challenges posed by sparse gene expression matrices. Additionally, the incorporation of DEC in GDEC enables the partitioning of cell clusters within a lower-dimensional space, thereby mitigating the adverse effects of noise on clustering outcomes. GDEC constructs a model based on existing scRNA-seq datasets and then applying transfer learning techniques to fine-tune the model using a limited amount of prior knowledge gleaned from the target dataset. This empowers GDEC to adeptly cluster scRNA-seq data cross different species and batches. Through cross-species and cross-batch clustering experiments, we conducted a comparative analysis between GDEC and conventional packages. Furthermore, we implemented GDEC on the scRNA-seq data of uterine fibroids. Compared results obtained from the Seurat package, GDEC unveiled a novel cell type (epithelial cells) and identified a notable number of new pathways among various cell types, thus underscoring the enhanced analytical capabilities of GDEC. Availability and implementation: https://github.com/YuzhiSun/GDEC/tree/main.

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.

Transcriptional Landscape of Cotton Roots in Response to Salt Stress at Single-cell Resolution

Increasing soil salinization has led to severe losses of plant yield and quality. Thus, it is urgent to investigate the molecular mechanism of the salt stress response. In this study, we took systematically analyzed cotton root response to salt stress by single-cell transcriptomics technology; 56,281 high-quality cells were totally obtained from 5-days-old lateral root tips of Gossypium arboreum under natural growth and different salt-treatment conditions. Ten cell types with an array of novel marker genes were synthetically identified and confirmed with in situ RNA hybridization, and some specific-type cells of pesudotime analysis also pointed out their potential differentiation trajectory. The prominent changes of cell numbers responding to salt stress were observed on outer epidermal and inner endodermic cells, which were significantly enriched in response to stress, amide biosynthetic process, glutathione metabolism, and glycolysis/gluconeogenesis. Other functional aggregations were concentrated on plant-type primary cell wall biogenesis, defense response, phenylpropanoid biosynthesis and metabolic pathways by analyzing the abundant differentially expressed genes (DEGs) identified from multiple comparisons. Some candidate DEGs related with transcription factors and plant hormones responding to salt stress were also identified, of which the function of Ga03G2153, an annotated auxin-responsive GH3.6, was confirmed by using virus-induced gene silencing (VIGS). The GaGH3.6-silenced plants presented severe stress-susceptive phenotype, and suffered more serious oxidative damages by detecting some physiological and biochemical indexes, indicating that GaGH3.6 might participate in salt tolerance in cotton through regulating oxidation-reduction process. For the first time, a transcriptional atlas of cotton roots under salt stress were characterized at a single-cell resolution, which explored the cellular heterogeneityand differentiation trajectory, providing valuable insights into the molecular mechanism underlying stress tolerance in plants.

Single-cell RNA-Seq reveals transcriptional regulatory networks directing the development of mouse maxillary prominence

During vertebrate embryonic development, neural crest-derived ectomesenchyme within the maxillary prominences undergoes precisely coordinated proliferation and differentiation to give rise to diverse craniofacial structures, such as tooth and palate. However, the transcriptional regulatory networks underpinning such an intricate process have not been fully elucidated. Here, we perform single-cell RNA-Seq to comprehensively characterize the transcriptional dynamics during mouse maxillary development from embryonic day (E) 10.5-E14.5. Our single-cell transcriptome atlas of ∼28,000 cells uncovers mesenchymal cell populations representing distinct differentiating states and reveals their developmental trajectory, suggesting that the segregation of dental from the palatal mesenchyme occurs at E11.5. Moreover, we identify a series of key transcription factors (TFs) associated with mesenchymal fate transitions and deduce the gene regulatory networks directed by these TFs. Collectively, our study provides important resources and insights for achieving a systems-level understanding of craniofacial morphogenesis and abnormality.

The maturation and aging trajectory of Marchantia polymorpha at single-cell resolution

Bryophytes represent a sister to the rest of land plants. Despite their evolutionary importance and relatively simple body plan, a comprehensive understanding of the cell types and transcriptional states that underpin the temporal development of bryophytes has not been achieved. Using time-resolved single-cell RNA sequencing, we define the cellular taxonomy of Marchantia polymorpha across asexual reproduction phases. We identify two maturation and aging trajectories of the main plant body of M. polymorpha at single-cell resolution: the gradual maturation of tissues and organs along the tip-to-base axis of the midvein and the progressive decline of meristem activities in the tip along the chronological axis. Specifically, we observe that the latter aging axis is temporally correlated with the formation of clonal propagules, suggesting an ancient strategy to optimize allocation of resources to producing offspring. Our work thus provides insights into the cellular heterogeneity that underpins the temporal development and aging of bryophytes.

Understanding plant pathogen interactions using spatial and single-cell technologies

Plants are in contact with diverse pathogens and microorganisms. Intense investigation over the last 30 years has resulted in the identification of multiple immune receptors in model and crop species as well as signaling overlap in surface-localized and intracellular immune receptors. However, scientists still have a limited understanding of how plants respond to diverse pathogens with spatial and cellular resolution. Recent advancements in single-cell, single-nucleus and spatial technologies can now be applied to plant-pathogen interactions. Here, we outline the current state of these technologies and highlight outstanding biological questions that can be addressed in the future.

Analysis of meristems and plant regeneration at single-cell resolution

Rapid development of high-throughput single-cell RNA sequencing (scRNA-seq) technologies offers exciting opportunities to reveal new and rare cell types, previously hidden cell states, and continuous developmental trajectories. In this review, we first illustrate the ways in which scRNA-seq enables researchers to distinguish between distinct plant cell populations, delineate cell cycle continuums, and infer continuous differentiation trajectories of diverse cell types in shoots, roots, and floral and vascular meristems with unprecedented resolution. We then highlight the emerging power of scRNA-seq to dissect cell heterogeneity in regenerating tissues and uncover the cellular basis of cell reprogramming and stem cell commitment during plant regeneration. We conclude by discussing related outstanding questions in the field.

Mapping the transcriptional landscape of human white and brown adipogenesis using single-nuclei RNA-seq

Adipogenesis is key to maintaining organism-wide energy balance and healthy metabolic phenotype, making it critical to thoroughly comprehend its molecular regulation in humans. By single-nuclei RNA-sequencing (snRNA-seq) of over 20,000 differentiating white and brown preadipocytes, we constructed a high-resolution temporal transcriptional landscape of human white and brown adipogenesis. White and brown preadipocytes were isolated from a single individual's neck region, thereby eliminating inter-subject variability across two distinct lineages. These preadipocytes were also immortalized to allow for controlled, in vitro differentiation, allowing sampling of distinct cellular states across the spectrum of adipogenic progression. Pseudotemporal cellular ordering revealed the dynamics of ECM remodeling during early adipogenesis, and lipogenic/thermogenic response during late white/brown adipogenesis. Comparison with adipogenic regulation in murine models Identified several novel transcription factors as potential targets for adipogenic/thermogenic drivers in humans. Among these novel candidates, we explored the role of TRPS1 in adipocyte differentiation and showed that its knockdown impairs white adipogenesis in vitro. Key adipogenic and lipogenic markers revealed in our analysis were applied to analyze publicly available scRNA-seq datasets; these confirmed unique cell maturation features in recently discovered murine preadipocytes, and revealed inhibition of adipogenic expansion in humans with obesity. Overall, our study presents a comprehensive molecular description of both white and brown adipogenesis in humans and provides an important resource for future studies of adipose tissue development and function in both health and metabolic disease state.

RNA recording in single bacterial cells using reprogrammed tracrRNAs

Capturing an individual cell's transcriptional history is a challenge exacerbated by the functional heterogeneity of cellular communities. Here, we leverage reprogrammed tracrRNAs (Rptrs) to record selected cellular transcripts as stored DNA edits in single living bacterial cells. Rptrs are designed to base pair with sensed transcripts, converting them into guide RNAs. The guide RNAs then direct a Cas9 base editor to target an introduced DNA target. The extent of base editing can then be read in the future by sequencing. We use this approach, called TIGER (transcribed RNAs inferred by genetically encoded records), to record heterologous and endogenous transcripts in individual bacterial cells. TIGER can quantify relative expression, distinguish single-nucleotide differences, record multiple transcripts simultaneously and read out single-cell phenomena. We further apply TIGER to record metabolic bet hedging and antibiotic resistance mobilization in Escherichia coli as well as host cell invasion by Salmonella. Through RNA recording, TIGER connects current cellular states with past transcriptional states to decipher complex cellular responses in single cells.

Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation

Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn's disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.

Multiplexed single-cell 3D spatial gene expression analysis in plant tissue using PHYTOMap

Retrieving the complex responses of individual cells in the native three-dimensional tissue context is crucial for a complete understanding of tissue functions. Here, we present PHYTOMap (plant hybridization-based targeted observation of gene expression map), a multiplexed fluorescence in situ hybridization method that enables single-cell and spatial analysis of gene expression in whole-mount plant tissue in a transgene-free manner and at low cost. We applied PHYTOMap to simultaneously analyse 28 cell-type marker genes in Arabidopsis roots and successfully identified major cell types, demonstrating that our method can substantially accelerate the spatial mapping of marker genes defined in single-cell RNA-sequencing datasets in complex plant tissue.

STellaris: a web server for accurate spatial mapping of single cells based on spatial transcriptomics data

Single-cell RNA sequencing (scRNA-seq) provides insights into gene expression heterogeneities in diverse cell types underlying homeostasis, development and pathological states. However, the loss of spatial information hinders its applications in deciphering spatially related features, such as cell-cell interactions in a spatial context. Here, we present STellaris (https://spatial.rhesusbase.com), a web server aimed to rapidly assign spatial information to scRNA-seq data based on their transcriptomic similarity with public spatial transcriptomics (ST) data. STellaris is founded on 101 manually curated ST datasets comprising 823 sections across different organs, developmental stages and pathological states from humans and mice. STellaris accepts raw count matrix and cell type annotation of scRNA-seq data as the input, and maps single cells to spatial locations in the tissue architecture of properly matched ST section. Spatially resolved information for intercellular communications, such as spatial distance and ligand-receptor interactions (LRIs), are further characterized between annotated cell types. Moreover, we also expanded the application of STellaris in spatial annotation of multiple regulatory levels with single-cell multiomics data, using the transcriptome as a bridge. STellaris was applied to several case studies to showcase its utility of adding value to the ever-growing scRNA-seq data from a spatial perspective.

A protocol for single nucleus RNA-seq from frozen skeletal muscle

Single-cell technologies are a method of choice to obtain vast amounts of cell-specific transcriptional information under physiological and diseased states. Myogenic cells are resistant to single-cell RNA sequencing because of their large, multinucleated nature. Here, we report a novel, reliable, and cost-effective method to analyze frozen human skeletal muscle by single-nucleus RNA sequencing. This method yields all expected cell types for human skeletal muscle and works on tissue frozen for long periods of time and with significant pathological changes. Our method is ideal for studying banked samples with the intention of studying human muscle disease.

Reference genes for quantitative Arabidopsis single molecule RNA fluorescence in situ hybridization

Subcellular mRNA quantities and spatial distributions are fundamental for driving gene regulatory programmes. Single molecule RNA fluorescence in situ hybridization (smFISH) uses fluorescent probes to label individual mRNA molecules, thereby facilitating both localization and quantitative studies. Validated reference mRNAs function as positive controls and are required for calibration. Here we present selection criteria for the first set of Arabidopsis smFISH reference genes. Following sequence and transcript data assessments, four mRNA probe sets were selected for imaging. Transcript counts per cell, correlations with cell size, and corrected fluorescence intensities were all calculated for comparison. In addition to validating reference probe sets, we present sample preparation steps that can retain green fluorescent protein fluorescence, thereby providing a method for simultaneous RNA and protein detection. In summary, our reference gene analyses, modified protocol, and simplified quantification method together provide a firm foundation for future quantitative single molecule RNA studies in Arabidopsis root apical meristem cells.

Artificial intelligence and high-dimensional technologies in the theragnosis of systemic lupus erythematosus

Systemic lupus erythematosus is a complex, systemic autoimmune disease characterised by immune dysregulation. Pathogenesis is multifactorial, contributing to clinical heterogeneity and posing challenges for diagnosis and treatment. Although strides in treatment options have been made in the past 15 years, with the US Food and Drug Administration approval of belimumab in 2011, there are still many patients who have inadequate responses to therapy. A better understanding of underlying disease mechanisms with a holistic and multiparametric approach is required to improve clinical assessment and treatment. This Review discusses the evolution of genomics, epigenomics, transcriptomics, and proteomics in the study of systemic lupus erythematosus and ways to amalgamate these silos of data with a systems-based approach while also discussing ways to strengthen the overall process. These mechanistic insights will facilitate the discovery of functionally relevant biomarkers to guide rational therapeutic selection to improve patient outcomes.

Single-cell transcriptome in silico analysis reveals conserved regulatory programs in macrophages/monocytes of abdominal aortic aneurysm from multiple mouse models and human

Abdominal aortic aneurysm (AAA) is a life-threatening disease and there is currently a lack of effective treatment to prevent it rupturing. ScRNA-seq studies of AAA are still lacking. In the study, we analyzed the published AAA scRNA-seq datasets from the mouse elastase-induced model, CaCl₂ treatment model, Ang II-induced model and human by using bioinformatic approaches and in silico analysis. A total of 26 cell clusters were obtained and 11 cell types were identified from multiple mouse AAA models. Also, the proportion of Mφ/Mo increased in the AAA group and Mφ/Mo was divided into seven subtypes. There were significant differences in transcriptional regulation patterns of Mφ/Mo in different AAA models. The enrichment pathways of upregulated or downregulated genes from Mφ/Mo in the three mouse datasets were different. The actived regulons of Mφ/Mo had strong specificity and the repressed regulons showed high consistency. The co-upregulated genes as well as actived regulons and co-downregulated genes as well as repressed regulons were closely correlated and formed regulatory networks. Mφ/Mo from human AAA dataset was divided into five subtypes. The proportion of three macrophage subpopulations increased but the proportion of two monocyte subpopulations decreased. In the AAA group, the upregulated or downregulated genes of Mφ/Mo were enriched in different pathways. After further analyzing the genes in Mφ/Mo of both mouse and human scRNA-seq datasets, two genes were upregulated in the four datasets, IL-1B and THBS1. In conclusion, in silico analysis of scRNA-seq revealed that Mφ/Mo and their regulatory related genes as well as interaction networks played an important role in the pathogenesis of AAA.

Towards a hierarchical gene regulatory network underlying somatic embryogenesis

Genome-editing technologies have advanced in recent years but designing future crops remains limited by current methods of improving somatic embryogenesis (SE) capacity. In this Opinion, we provide an update on the molecular event by which the phytohormone auxin promotes the acquisition of plant cell totipotency through evoking massive changes in transcriptome and chromatin accessibility. We propose that the chromatin states and individual totipotency-related transcription factors (TFs) from disparate gene families organize into a hierarchical gene regulatory network underlying SE. We conclude with a discussion of the practical paths to probe the cellular origin of the somatic embryo and the epigenetic landscape of the totipotent cell state in the era of single-cell genomics.

Single-nuclei transcriptome analysis of the shoot apex vascular system differentiation in Populus

Differentiation of stem cells in the plant apex gives rise to aerial tissues and organs. Presently, we lack a lineage map of the shoot apex cells in woody perennials - a crucial gap considering their role in determining primary and secondary growth. Here, we used single-nuclei RNA-sequencing to determine cell type-specific transcriptomes of the Populus vegetative shoot apex. We identified highly heterogeneous cell populations clustered into seven broad groups represented by 18 transcriptionally distinct cell clusters. Next, we established the developmental trajectories of the epidermis, leaf mesophyll and vascular tissue. Motivated by the high similarities between Populus and Arabidopsis cell population in the vegetative apex, we applied a pipeline for interspecific single-cell gene expression data integration. We contrasted the developmental trajectories of primary phloem and xylem formation in both species, establishing the first comparison of vascular development between a model annual herbaceous and a woody perennial plant species. Our results offer a valuable resource for investigating the principles underlying cell division and differentiation conserved between herbaceous and perennial species while also allowing us to examine species-specific differences at single-cell resolution.

[Characterization of the tumor microenvironment by highly multiplexed microscopy]

Malignant neoplasms are highly complex ecosystems consisting of tumor cells and the tumor microenvironment (TME), which is composed of structural elements (vessels, fibroblasts, extracellular matrix) and a wide variety of infiltrating immune cell types of the innate and adaptive immune systems. The TME is the main site of tumor cell-immune cell interactions and plays a critical role in antitumoral immunity. Immunotherapies can affect the interactions between immune cell types and tumor cells in the TME, boost immune responses, and lead to tumor elimination. Novel highly multiplexed microscopy techniques, which enable the detection of more than 50 simultaneous markers in tissues, facilitate the in-depth characterization of the TME at single-cell resolution in clinically relevant samples. Detailed knowledge about the cellular and spatial composition of the TME, the specific cell types and their functional properties, and cell-cell interactions-as revealed by highly multiplexed microscopy-will improve our understanding of immunotherapies' mechanisms of action and reveal new potential therapeutic targets and predictive biomarkers.

Optocoder: computational decoding of spatially indexed bead arrays

Advancing technologies that quantify gene expression in space are transforming contemporary biology research. A class of spatial transcriptomics methods uses barcoded bead arrays that are optically decoded via microscopy and are later matched to sequenced data from the respective libraries. To obtain a detailed representation of the tissue in space, robust and efficient computational pipelines are required to process microscopy images and accurately basecall the bead barcodes. Optocoder is a computational framework that processes microscopy images to decode bead barcodes in space. It efficiently aligns images, detects beads, and corrects for confounding factors of the fluorescence signal, such as crosstalk and phasing. Furthermore, Optocoder employs supervised machine learning to strongly increase the number of matches between optically decoded and sequenced barcodes. We benchmark Optocoder using data from an in-house spatial transcriptomics platform, as well as from Slide-Seq(V2), and we show that it efficiently processes all datasets without modification. Optocoder is publicly available, open-source and provided as a stand-alone Python package on GitHub: https://github.com/rajewsky-lab/optocoder.

A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data

Cellular heterogeneity in growth and differentiation results in organ patterning. Single-cell transcriptomics allows characterization of gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during this methodology. To recover the original location of cells in the developing organ is essential to link gene activity with cellular identity and function in plants. Here, we propose a method to reconstruct genome-wide gene expression patterns of individual cells in a 3D flower meristem by combining single-nuclei RNA-seq with microcopy-based 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the method is used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com.

Single-cell transcriptomics allows gene expression heterogeneity to be assessed at cellular resolution but the original location of each cell is unknown. Here the authors combine single nuclei RNA-seq with 3D spatial reconstruction of floral meristems to link gene activities with morphology.

Single-cell dual-omics reveals the transcriptomic and epigenomic diversity of cardiac non-myocytes

AIMS

The precise cellular identity and molecular features of non-myocytes (non-CMs) in a mammalian heart at a single-cell level remain elusive. Depiction of epigenetic landscape with transcriptomic signatures using the latest single-cell multi-omics has the potential to unravel the molecular programs underlying the cellular diversity of cardiac non-myocytes. Here, we characterized the molecular and cellular features of cardiac non-CM populations in the adult murine heart at the single-cell level.

METHODS AND RESULTS

Through single-cell dual omics analysis, we mapped the epigenetic landscapes, characterized the transcriptomic profiles and delineated the molecular signatures of cardiac non-CMs in the adult murine heart. Distinct cis-regulatory elements and trans-acting factors for the individual major non-CM cell types (endothelial cells, fibroblast, pericytes, and immune cells) were identified. In particular, unbiased sub-clustering and functional annotation of cardiac fibroblasts (FBs) revealed extensive FB heterogeneity and identified FB sub-types with functional states related to the cellular response to stimuli, cytoskeleton organization, and immune regulation, respectively. We further explored the function of marker genes Hsd11b1 and Gfpt2 that label major FB subpopulations and determined the distribution of Hsd11b1+ and Gfp2+ FBs in murine healthy and diseased hearts.

CONCLUSIONS

In summary, we characterized the non-CM cellular identity at the transcriptome and epigenome levels using single-cell omics approaches and discovered previously unrecognized cardiac fibroblast subpopulations with unique functional states.

Bibliometric review of ATAC-Seq and its application in gene expression

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single-cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.

Characterization of transcript enrichment and detection bias in single-nucleus RNA-seq for mapping of distinct human adipocyte lineages

Single-cell RNA sequencing (scRNA-seq) enables molecular characterization of complex biological tissues at high resolution. The requirement of single-cell extraction, however, makes it challenging for profiling tissues such as adipose tissue, for which collection of intact single adipocytes is complicated by their fragile nature. For such tissues, single-nucleus extraction is often much more efficient and therefore single-nucleus RNA sequencing (snRNA-seq) presents an alternative to scRNA-seq. However, nuclear transcripts represent only a fraction of the transcriptome in a single cell, with snRNA-seq marked with inherent transcript enrichment and detection biases. Therefore, snRNA-seq may be inadequate for mapping important transcriptional signatures in adipose tissue. In this study, we compare the transcriptomic landscape of single nuclei isolated from preadipocytes and mature adipocytes across human white and brown adipocyte lineages, with whole-cell transcriptome. We show that snRNA-seq is capable of identifying the broad cell types present in scRNA-seq at all states of adipogenesis. However, we also explore how and why the nuclear transcriptome is biased and limited, as well as how it can be advantageous. We robustly characterize the enrichment of nuclear-localized transcripts and adipogenic regulatory lncRNAs in snRNA-seq, while also providing a detailed understanding for the preferential detection of long genes upon using this technique. To remove such technical detection biases, we propose a normalization strategy for a more accurate comparison of nuclear and cellular data. Finally, we show successful integration of scRNA-seq and snRNA-seq data sets with existing bioinformatic tools. Overall, our results illustrate the applicability of snRNA-seq for the characterization of cellular diversity in the adipose tissue.

The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues

As sessile organisms, plants are finely tuned to respond dynamically to developmental, circadian and environmental cues. Genome-wide studies investigating these types of cues have uncovered the intrinsically different ways they can impact gene expression over time. Recent advances in single-cell sequencing and time-based bioinformatic algorithms are now beginning to reveal the dynamics of these time-based responses within individual cells and plant tissues. Here, we review what these techniques have revealed about the spatiotemporal nature of gene regulation, paying particular attention to the three distinct ways in which plant tissues are time sensitive. (i) First, we discuss how studying plant cell identity can reveal developmental trajectories hidden in pseudotime. (ii) Next, we present evidence that indicates that plant cell types keep their own local time through tissue-specific regulation of the circadian clock. (iii) Finally, we review what determines the speed of environmental signaling responses, and how they can be contingent on developmental and circadian time. By these means, this review sheds light on how these different scales of time-based responses can act with tissue and cell-type specificity to elicit changes in whole plant systems.

Subcellular Transcriptomics and Proteomics: A Comparative Methods Review

The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics.

Crafting a blueprint for single-cell RNA sequencing

Droplet-based single-cell RNA sequencing (scRNA-Seq) has moved rapidly from a technology with great potential to a method applied to ever-broader questions. The detailed information that scRNA-Seq offers has proven incredibly powerful in resolving cell responses to developmental and environmental cues. However, to maximize the potential of this technology, a panoply of upstream, practical points require consideration. Principal among these are the optimization of cell-isolation procedures, accommodating biotic/abiotic stress responses, and discerning the number of cells and sequencing reads needed. To complement excellent reviews outlining applications and data analysis tools for scRNA-Seq, we here discuss these considerations and provide practical tips to tailor experimental design and ensure the best possible outcome.

Single-cell analysis of cell identity in the Arabidopsis root apical meristem: insights and opportunities

A fundamental question in developmental biology is how the progeny of stem cells become differentiated tissues. The Arabidopsis root is a tractable model to address this question due to its simple organization and defined cell lineages. In particular, the zone of dividing cells at the root tip-the root apical meristem-presents an opportunity to map the gene regulatory networks underlying stem cell niche maintenance, tissue patterning, and cell identity acquisition. To identify molecular regulators of these processes, studies over the last 20 years employed global profiling of gene expression patterns. However, these technologies are prone to information loss due to averaging gene expression signatures over multiple cell types and/or developmental stages. Recently developed high-throughput methods to profile gene expression at single-cell resolution have been successfully applied to plants. Here, we review insights from the first published single-cell mRNA sequencing and chromatin accessibility datasets generated from Arabidopsis roots. These studies successfully reconstruct developmental trajectories, phenotype cell identity mutants at unprecedented resolution, and reveal cell type-specific responses to environmental stimuli. The experimental insight gained from Arabidopsis paves the way to profile roots from additional species.

Plant single-cell solutions for energy and the environment

Progress in sequencing, microfluidics, and analysis strategies has revolutionized the granularity at which multicellular organisms can be studied. In particular, single-cell transcriptomics has led to fundamental new insights into animal biology, such as the discovery of new cell types and cell type-specific disease processes. However, the application of single-cell approaches to plants, fungi, algae, or bacteria (environmental organisms) has been far more limited, largely due to the challenges posed by polysaccharide walls surrounding these species' cells. In this perspective, we discuss opportunities afforded by single-cell technologies for energy and environmental science and grand challenges that must be tackled to apply these approaches to plants, fungi and algae. We highlight the need to develop better and more comprehensive single-cell technologies, analysis and visualization tools, and tissue preparation methods. We advocate for the creation of a centralized, open-access database to house plant single-cell data. Finally, we consider how such efforts should balance the need for deep characterization of select model species while still capturing the diversity in the plant kingdom. Investments into the development of methods, their application to relevant species, and the creation of resources to support data dissemination will enable groundbreaking insights to propel energy and environmental science forward.

NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport

Single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized modern biomedical sciences. A fundamental challenge is to incorporate spatial information to study tissue organization and spatial gene expression patterns. Here, we describe a detailed protocol for using novoSpaRc, a computational framework that probabilistically assigns cells to tissue locations. At the core of this framework lies a structural correspondence hypothesis, that cells in physical proximity share similar gene expression profiles. Given scRNA-seq data, novoSpaRc spatially reconstructs tissues based on this hypothesis, and optionally, by including a reference atlas of marker genes to improve reconstruction. We describe the novoSpaRc algorithm, and its implementation in an open-source Python package ( https://pypi.org/project/novosparc ). NovoSpaRc maps a scRNA-seq dataset of 10,000 cells onto 1,000 locations in <5 min. We describe results obtained using novoSpaRc to reconstruct the mouse organ of Corti de novo based on the structural correspondence assumption and human osteosarcoma cultured cells based on marker gene information, and provide a step-by-step guide to Drosophila embryo reconstruction in the Procedure to demonstrate how these two strategies can be combined.

Quantitative imaging of RNA polymerase II activity in plants reveals the single-cell basis of tissue-wide transcriptional dynamics

The responses of plants to their environment are often dependent on the spatiotemporal dynamics of transcriptional regulation. While live-imaging tools have been used extensively to quantitatively capture rapid transcriptional dynamics in living animal cells, the lack of implementation of these technologies in plants has limited concomitant quantitative studies in this kingdom. Here, we applied the PP7 and MS2 RNA-labelling technologies for the quantitative imaging of RNA polymerase II activity dynamics in single cells of living plants as they respond to experimental treatments. Using this technology, we counted nascent RNA transcripts in real time in Nicotiana benthamiana (tobacco) and Arabidopsis thaliana. Examination of heat shock reporters revealed that plant tissues respond to external signals by modulating the proportion of cells that switch from an undetectable basal state to a high-transcription state, instead of modulating the rate of transcription across all cells in a graded fashion. This switch-like behaviour, combined with cell-to-cell variability in transcription rate, results in mRNA production variability spanning three orders of magnitude. We determined that cellular heterogeneity stems mainly from stochasticity intrinsic to individual alleles instead of variability in cellular composition. Together, our results demonstrate that it is now possible to quantitatively study the dynamics of transcriptional programs in single cells of living plants.

Generation and Characterization of a Cell Type-Specific, Inducible Cre-Driver Line to Study Olfactory Processing

In sensory systems of the brain, mechanisms exist to extract distinct features from stimuli to generate a variety of behavioral repertoires. These often correspond to different cell types at various stages in sensory processing. In the mammalian olfactory system, complex information processing starts in the olfactory bulb, whose output is conveyed by mitral cells (MCs) and tufted cells (TCs). Despite many differences between them, and despite the crucial position they occupy in the information hierarchy, Cre-driver lines that distinguish them do not yet exist. Here, we sought to identify genes that are differentially expressed between MCs and TCs of the mouse, with an ultimate goal to generate a cell type-specific Cre-driver line, starting from a transcriptome analysis using a large and publicly available single-cell RNA-seq dataset (Zeisel et al., 2018). Many genes were differentially expressed, but only a few showed consistent expressions in MCs and at the specificity required. After further validating these putative markers using ISH, two genes (i.e., Pkib and Lbdh2) remained as promising candidates. Using CRISPR/Cas9-mediated gene editing, we generated Cre-driver lines and analyzed the resulting recombination patterns. This indicated that our new inducible Cre-driver line, Lbhd2-CreERT2, can be used to genetically label MCs in a tamoxifen dose-dependent manner, both in male and female mice, as assessed by soma locations, projection patterns, and sensory-evoked responses in vivo Hence, this is a promising tool for investigating cell type-specific contributions to olfactory processing and demonstrates the power of publicly accessible data in accelerating science.SIGNIFICANCE STATEMENT In the brain, distinct cell types play unique roles. It is therefore important to have tools for studying unique cell types specifically. For the sense of smell in mammals, information is processed first by circuits of the olfactory bulb, where two types of cells, mitral cells and tufted cells, output different information. We generated a transgenic mouse line that enables mitral cells to be specifically labeled or manipulated. This was achieved by looking for genes that are specific to mitral cells using a large and public gene expression dataset, and creating a transgenic mouse using the gene editing technique, CRISPR/Cas9. This will allow scientists to better investigate parallel information processing underlying the sense of smell.

scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

Motivation

Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data.

Results

Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data.

Availability and implementation

The R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997.

Supplementary information

Supplementary data are available at Bioinformatics online.

Single-cell nucleic acid profiling in droplets (SNAPD) enables high-throughput analysis of heterogeneous cell populations

Experimental methods that capture the individual properties of single cells are revealing the key role of cell-to-cell variability in countless biological processes. These single-cell methods are becoming increasingly important across the life sciences in fields such as immunology, regenerative medicine and cancer biology. In addition to high-dimensional transcriptomic techniques such as single-cell RNA sequencing, there is a need for fast, simple and high-throughput assays to enumerate cell samples based on RNA biomarkers. In this work, we present single-cell nucleic acid profiling in droplets (SNAPD) to analyze sets of transcriptional markers in tens of thousands of single mammalian cells. Individual cells are encapsulated in aqueous droplets on a microfluidic chip and the RNA markers in each cell are amplified. Molecular logic circuits then integrate these amplicons to categorize cells based on the transcriptional markers and produce a detectable fluorescence output. SNAPD is capable of analyzing over 100,000 cells per hour and can be used to quantify distinct cell types within heterogeneous populations, detect rare cells at frequencies down to 0.1% and enrich specific cell types using microfluidic sorting. SNAPD provides a simple, rapid, low cost and scalable approach to study complex phenotypes in heterogeneous cell populations.