Benchmarking single-cell RNA-sequencing protocols for cell atlas projects

nf-core/marsseq: systematic preprocessing pipeline for MARS-seq experiments

Motivation

Single sequencing technology (scRNA-seq) enables the study of gene regulation at a single cell level. Although many sc-RNA-seq protocols have been established, they have varied in technical complexity, sequencing depth and multimodal capabilities leading to shared limitations in data interpretation due to a lack of standardized preprocessing and consistent data reproducibility. While plate based techniques such as Massively Parallel RNA Single cell Sequencing (MARS-seq2.0) provide reference data on the cells that will be sequenced, the data format limits the possible analysis. Here, we focus on the standardization of MARS-seq analysis and its applicability to RNA velocity.

Results

We have taken the original MARS-seq2.0 pipeline and revised it to enable implementation using the nf-core framework. By doing so, we have simplified pipeline execution, enabling a streamlined application with increased transparency and scalability. We have incorporated additional checkpoints to verify experimental metadata and improved the pipeline by implementing a custom workflow for RNA velocity estimation. The pipeline is part of the nf-core bioinformatics community and is freely available at https://github.com/nfcore/marsseq with data analysis at https://github.com/brickmanlab/proks-et-al-2023.

Availability and implementation

We introduce an updated preprocessing pipeline for MARS-seq experiments following state-of-the-art guidelines for scientific software development with the added ability to infer RNA velocity.

Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics

Automatic cell-type annotation methods assign cell-type labels to new, unlabeled datasets by leveraging relationships from a reference RNA-seq atlas. However, new datasets may include labels absent from the reference dataset or exhibit feature distributions that diverge from it. These scenarios can significantly affect the reliability of cell type predictions, a factor often overlooked in current automatic annotation methods. The field of out-of-distribution detection (OOD), primarily focused on computer vision, addresses the identification of instances that differ from the training distribution. Therefore, the implementation of OOD methods in the context of novel cell type annotation and data shift detection for single-cell transcriptomics may enhance annotation accuracy and trustworthiness. We evaluate six OOD detection methods: LogitNorm, MC dropout, Deep Ensembles, Energy-based OOD, Deep NN, and Posterior networks, for their annotation and OOD detection performance in both synthetical and real-life application settings. We show that OOD detection methods can accurately identify novel cell types and demonstrate potential to detect significant data shifts in non-integrated datasets. Moreover, we find that integration of the OOD datasets does not interfere with OOD detection of novel cell types.

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.

Macrophages and cardiac lesion in zebrafish: what can single-cell RNA sequencing reveal?

Unlike mammals, zebrafish can regenerate their heart after cardiac insult. There are several ways to perform cardiac injury in zebrafish, but cryoinjury most closely resembles human myocardial infarction (MI). Studies demonstrated that macrophages are essential cells from the beginning to later stages of cardiac injury throughout the regenerative process in zebrafish. These cells have phenotypic plasticity; hence, overly sensitive techniques, such as single-cell RNA sequencing (scRNAseq), are essential for uncovering the phenotype needed for zebrafish cardiac injury regeneration, from inflammatory profile initiation to scar resolution. This technique enables the RNA sequencing of individual cells, thus generating clusters of cells with similar gene expression and allowing the study of a particular cell population. Therefore, in this review, we focused on discussing data obtained by scRNAseq of macrophages in the context of cardiac injury. We found that from 1 to 7 days post-injury (dpi), macrophages are present with inflammatory and reparative functions in either cryoinjury or ventricular resection. At 14 dpi, there were differences between the injury models, especially in the expression profile of inflammatory cytokines, and studies with later time points are needed to understand the gene expression that enrolls the collagen scar resorption dynamic.

The spatial transcriptome of the late-stage embryonic and postnatal mouse brain reveals spatiotemporal molecular markers

The neocortical development process includes cell proliferation, differentiation, migration, and maturation, supported by precise genetic regulation. To understand these processes at the cellular and molecular levels, it is necessary to characterize the fundamental anatomical structures by gene expression. However, markers established in the adult brain sometimes behave differently in the fetal brain, actively changing during development. The spatial transcriptome is a powerful analytical method that enables sequence analysis while retaining spatial information. However, a deeper understanding of these data requires computational estimation, including integration with single-cell transcriptome data and aggregation of spots at the single-cell cluster level. The application of such analysis to biomarker discovery has only begun recently, and its application to the developing fetal brain is largely unexplored. In this study, we performed a spatial transcriptome analysis of the developing mouse brain to investigate spatio-temporal regulation of gene expression during development. Using these data, we conducted an integrated study with publicly available mouse data sets. Our data-driven analysis identified novel molecular markers of the choroid plexus, piriform cortex, and thalamus. Furthermore, we identified a novel molecular marker that can determine the dorsal endopiriform nucleus (DEn) of the developmental stage in the claustrum/DEn complex.

Feature selection methods affect the performance of scRNA-seq data integration and querying

The availability of single-cell transcriptomics has allowed the construction of reference cell atlases, but their usefulness depends on the quality of dataset integration and the ability to map new samples. Previous benchmarks have compared integration methods and suggest that feature selection improves performance but have not explored how best to select features. Here, we benchmark feature selection methods for single-cell RNA sequencing integration using metrics beyond batch correction and preservation of biological variation to assess query mapping, label transfer and the detection of unseen populations. We reinforce common practice by showing that highly variable feature selection is effective for producing high-quality integrations and provide further guidance on the effect of the number of features selected, batch-aware feature selection, lineage-specific feature selection and integration and the interaction between feature selection and integration models. These results are informative for analysts working on large-scale tissue atlases, using atlases or integrating their own data to tackle specific biological questions.

Single-Cell Analysis in Cerebrovascular Research: Primed for Breakthroughs and Clinical Impact

Data generated using single-cell RNA-sequencing has the potential to transform understanding of the cerebral circulation and advance clinical care. However, the high volume of data, sometimes generated and presented without proper pathophysiological context, can be difficult to interpret and integrate into current understanding of the cerebral circulation and its disorders. Furthermore, heterogeneity in the representation of brain regions and vascular segments makes it difficult to compare results across studies. There are currently no standards for tissue collection and processing that allow easy comparisons across studies and analytical platforms. There are no standards either for single-cell data analysis and presentation. This topical review introduces single-cell RNA-sequencing to physicians and scientists in the cerebrovascular field, with the goals of highlighting opportunities and challenges of applying this technology in the cerebrovascular field and discussing key concepts and knowledge gaps that can be addressed by single-cell RNA-sequencing.

Application of bioinformatic tools in cell type classification for single-cell RNA-seq data

The advancements in single-cell RNA sequencing (scRNAseq) technology have significantly transformed genomics research, enabling the handling of thousands of cells in each experiment. As of now, 32,068 research studies have been cataloged in the Pubmed database. The primary aim of scRNAseq investigations is to identify cell types, understand the antitumor immune response, and identify new and uncommon cell types. Traditional techniques for identifying cell types include microscopy, histology, and pathological characteristics. However, the complexity of instruments and the need for precise experimental design make it difficult to fully capture the overall heterogeneity. Unsupervised clustering and supervised classification methods have been used to solve this task. Supervised cell type classification methods have gained popularity as large-scale, high-quality, well-annotated and more robust results compared to clustering methods. A recent study showed that support vector machine (SVM) gives a high-quality classification performance in different scenarios. In this article, we compare and evaluate the performance of four different kernels (sigmoid, linear, radial, polynomial) of SVM. The results of the experiments on three standard scRNA-seq datasets indicate that SVM with linear and SVM with sigmoid kernel classify the cells more accurately (approx. 99 %) where SVM linear kernel method has remarkably fast computation time and we also evaluate the results using some single cell specific evaluation matrices F-1 score, MCC, AUC value. Additionally, it sheds light on the potential use of kernels of SVM to give underlying information of single-cell RNA-Seq data more effectively.

Biomarker-informed care for patients with renal cell carcinoma

Kidney cancer is a commonly diagnosed cancer in adults, and clear cell renal cell carcinoma (ccRCC) is the most common histological subtype. Immune checkpoint inhibitors have revolutionized care for patients with ccRCC, either as adjuvant therapy or combined with other agents in advanced disease. However, biomarkers to predict therapeutic benefits are lacking. Here, we explore biomarkers that predict therapeutic response in other tumor types and discuss the reasons for their ineffectiveness in ccRCC. We also review emerging predictive and prognostic biomarkers to prioritize in ccRCC, including gene expression signatures.

A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines

The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N6-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N6-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.

A systematic review of single-cell RNA sequencing applications and innovations

Bulk RNA sequencing is one type of RNA sequencing technique, as well as targeted RNA sequencing and whole transcriptome sequencing. It provides valuable insights into gene expression in specific cell populations or regions. However, these methods often miss the diversity of cells within complex tissues. This restriction is overcome by single-cell RNA sequencing, which records gene expression at the single-cell level. It offers a detailed picture of the diversity of cells. It is essential to study glucose homeostasis. It offers thorough explanations of cellular variation. Networks and Governance Dynamics The use of scRNA-seq in islet cells is reviewed in this study, along with sample preparation, sequencing, and computational analysis. It highlights advances in understanding cell types. Gene activity and cell interactions. Along with the challenges and limitations of scRNA-seq, this review highlights the importance of scRNA-seq in understanding complex biological processes and diseases. It is an essential resource for future research and method development in this field, which will help to build personalized treatment.

Neutrophil-related IL1R2 gene predicts the occurrence and early progression of myocardial infarction

Introduction

Myocardial infarction (MI) is a leading cause of death worldwide. Immune cells play a significant role in the MI development. This study aims to identify a marker related to neutrophil for the diagnosis and early progression of MI.

Methods

Key genes were screened using three machine learning algorithms to establish a diagnostic model. A gene associated with the early progression of MI was identified based on single cell RNA sequencing data. To further validate the predictive value of the gene, the mouse models of MI were constructed. Immunofluorescence (IF) analysis demonstrated the co-expression of the gene with neutrophils. Immunohistochemistry (IHC) was performed to validate the role of the gene in the progression of MI.

Results

Neutrophils were identified and verified as the key infiltrating immune cells (IICs) involved in the onset of MI. A diagnostic panel with superior performance was developed using five key genes related to neutrophils in MI (AUC = 0.887). Among the panel, IL1R2 was found to early phase of MI, which was further corroborated by IHC in mouse models of MI.

Conclusions

This study suggests that IL1R2, which is specific to neutrophils, can predict the diagnosis and early progression of MI, providing new insights into the clinical management of MI.

CellBouncer, A Unified Toolkit for Single-Cell Demultiplexing and Ambient RNA Analysis, Reveals Hominid Mitochondrial Incompatibilities

Pooled processing, in which cells from multiple sources are cultured or captured together, is an increasingly popular strategy for droplet-based single cell sequencing studies. This design allows efficient scaling of experiments, isolation of cell-intrinsic differences, and mitigation of batch effects. We present CellBouncer, a computational toolkit for demultiplexing and analyzing single-cell sequencing data from pooled experiments. We demonstrate that CellBouncer can separate and quantify multi-species and multi-individual cell mixtures, identify unknown mitochondrial haplotypes in cells, assign treatments from lipid-conjugated barcodes or CRISPR sgRNAs, and infer pool composition, outperforming existing methods. We also introduce methods to quantify ambient RNA contamination per cell, infer individual donors' contributions to the ambient RNA pool, and determine a consensus doublet rate harmonized across data types. Applying these tools to tetraploid composite cells, we identify a competitive advantage of human over chimpanzee mitochondria across 10 cell fusion lines and provide evidence for inter-mitochondrial incompatibility and mito-nuclear incompatibility between species.

Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas

The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. We generated a benchmarking dataset for the renal cortex by integrating 3' and 5' scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we developed the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assessed integration strategies. "Horizontal" integration of scRNA and snRNA-seq improved cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq had an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration was especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.

Disruption of circadian intraocular pressure fluctuations in mice by the Lyst beige-J mutation

Intraocular pressure (IOP) follows a circadian rhythm. In both humans and mice, IOP is normally slightly elevated at night during the dark phase of the light cycle. In studying a strain of mice for possible indices of glaucoma, we incidentally discovered that C57BL/6J mice homozygous for the beige-J mutation of the Lyst gene lack a circadian fluctuation in IOP. Instead of having an elevated dark phase IOP, homozygotes exhibit a uniform IOP characteristic for light period values of C57BL/6J mice. The beige-J mutation results from deletion of a single isoleucine amino acid in the LYST WD40 motif likely to influence protein-protein interactions. Based on the literature, we hypothesized that CSNK2B (casein kinase 2, beta polypeptide) might be a relevant interacting protein, which we confirmed with a pulldown assay as a binding partner of wild-type, but not beige-J encoding, LYST protein. Treating wild-type mice with 4,5,6,7-tetrabromobenzotriazole (TBB), a casein kinase 2 inhibitor, recapitulated the beige-J mutant phenotype in preventing a rise in IOP during the dark period. Together, these results identify Lyst beige-J mice as a new strain for studying circadian IOP regulation and point to casein kinase 2 as a key molecule of interest.

Superloaded Multiplexed scRNA-seq Data Preserves Primary Immune Cell Heterogeneity but Necessitates Stringent Doublet Removal

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) has improved our ability to characterize rare cell populations. In practice, cells from different tissues or donors are simultaneously loaded onto the instrument (multiplexed) at the recommended (standard loading) or higher (superloading) numbers to save time and money. Although cost-effective, superloading can stymie computational analyses owing to high multiplet rates and sample complexity.

METHODS

We compared the effects of superloading on multiplexed single-cell gene expression and T cell receptor (TCR) data generated from human thymus and blood samples from different donors.

RESULTS

Minimal transcriptomic differences were observed between the data generated by either standard or superloading. Irrespective of the loading cell number, we found that over 50% of the T cells expressing multiple TCR chains were doublets.

CONCLUSION

Multiple samples can be run simultaneously without compromising data quality and subsequent analyses. However, an additional doublet removal step based on TCR configuration may improve the accuracy of T cell analysis.

Diffusion Smart-seq3 of breast cancer spheroids to explore spatial tumor biology and test evolutionary principles of tumor heterogeneity

Combining 3D cultures such as tumor spheroids and organoids with spatial omics holds great potential for tissue biology and cancer research. Yet, this potential is presently limited by technical and financial challenges of spatial omics methods and 3D cultures. To address this, we combine dye diffusion, the Smart-seq3xpress protocol for deep single-cell gene expression profiling, and dedicated probabilistic inference methods into diffusion Smart-seq3 (Smart-seq3D), to reveal the transcriptome of single cells along with their position along the core-periphery axis of spheroids. Applying Smart-seq3D to triple-negative breast tumor spheroids identifies thousands of spatial genes and reveals continuous, ungated spatial gene expression. Spatial gene and pathway expression patterns suggest biologies specific to spheroid regions, which we validate by immunostainings and pharmacological interventions. We use the Smart-seq3D data to test evolutionary principles of spatial tumor heterogeneity. Finally, we characterize aspects of tumor heterogeneity captured by 3D spheroids that are missing from 2D cultures but found in tumors in vivo. Smart-seq3D can offer a cost-efficient approach to explore how cells adapt their transcriptome to different micro-environments, reveal spatial determinants of drug resistance and could serve to characterize spatial interactions between cancer and stromal/immune cells in 3D co-cultures.

A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samples

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we utilize an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using this well-matched, that is, benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using two benchmark data sets of healthy retinas and ovarian cancer tissues suggest much-improved deconvolution accuracy. Leveraging tissue-specific benchmark data sets, we applied DeMixSC to a large cohort of 453 age-related macular degeneration patients and a cohort of 30 ovarian cancer patients with various responses to neoadjuvant chemotherapy. Only DeMixSC successfully unveiled biologically meaningful differences across patient groups, demonstrating its broad applicability in diverse real-world clinical scenarios. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched data set to resolve this challenge. The developed DeMixSC framework is generally applicable for accurately deconvolving large cohorts of disease tissues, including cancers, when a well-matched benchmark data set is available.

Applications of Nanotechnology for Spatial Omics: Biological Structures and Functions at Nanoscale Resolution

Spatial omics methods are extensions of traditional histological methods that can illuminate important biomedical mechanisms of physiology and disease by examining the distribution of biomolecules, including nucleic acids, proteins, lipids, and metabolites, at microscale resolution within tissues or individual cells. Since, for some applications, the desired resolution for spatial omics approaches the nanometer scale, classical tools have inherent limitations when applied to spatial omics analyses, and they can measure only a limited number of targets. Nanotechnology applications have been instrumental in overcoming these bottlenecks. When nanometer-level resolution is needed for spatial omics, super resolution microscopy or detection imaging techniques, such as mass spectrometer imaging, are required to generate precise spatial images of target expression. DNA nanostructures are widely used in spatial omics for purposes such as nucleic acid detection, signal amplification, and DNA barcoding for target molecule labeling, underscoring advances in spatial omics. Other properties of nanotechnologies include advanced spatial omics methods, such as microfluidic chips and DNA barcodes. In this review, we describe how nanotechnologies have been applied to the development of spatial transcriptomics, proteomics, metabolomics, epigenomics, and multiomics approaches. We focus on how nanotechnology supports improved resolution and throughput of spatial omics, surpassing traditional techniques. We also summarize future challenges and opportunities for the application of nanotechnology to spatial omics methods.

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

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

A comprehensive analysis framework for evaluating commercial single-cell RNA sequencing technologies

This study examined nine prominent commercially available single-cell RNA sequencing (scRNA-seq) kits across four technology groups. Each kit was characterized using peripheral blood mononuclear cells (PBMCs) from a single donor, which enabled consistent assessment of factors such as analytical performance, protocol duration and cost. The Chromium Fixed RNA Profiling kit from 10× Genomics, with its probe-based RNA detection method, demonstrated the best overall performance. The Rhapsody WTA kit from Becton Dickinson exhibited a balance between performance and cost. Importantly, we introduce the read utilization metric, which differentiates scRNA-seq kits based on the efficiency of converting sequencing reads into usable counts. Thus, read utilization is an important feature that substantially impacts sensitivity and cost. With data from 169, 262 cells, our work provides a comprehensive comparison of commercial scRNA-seq technologies to facilitate the effective implementation of single-cell studies.

Considerations for building and using integrated single-cell atlases

The rapid adoption of single-cell technologies has created an opportunity to build single-cell 'atlases' integrating diverse datasets across many laboratories. Such atlases can serve as a reference for analyzing and interpreting current and future data. However, it has become apparent that atlasing approaches differ, and the impact of these differences are often unclear. Here we review the current atlasing literature and present considerations for building and using atlases. Importantly, we find that no one-size-fits-all protocol for atlas building exists, but rather we discuss context-specific considerations and workflows, including atlas conceptualization, data collection, curation and integration, atlas evaluation and atlas sharing. We further highlight the benefits of integrated atlases for analyses of new datasets and deriving biological insights beyond what is possible from individual datasets. Our overview of current practices and associated recommendations will improve the quality of atlases to come, facilitating the shift to a unified, reference-based understanding of single-cell biology.

Intestinal butyric acid-mediated disruption of gut hormone secretion and lipid metabolism in vasopressin receptor-deficient mice

OBJECTIVES

Arginine vasopressin (AVP), known as an antidiuretic hormone, is also crucial in metabolic homeostasis. Although AVP receptor-deficient mice exhibit various abnormalities in glucose and lipid metabolism, the mechanism underlying these symptoms remains unclear. This study aimed to explore the involvement of the gut hormones including glucagon-like peptide-1 (GLP-1) and microbiota as essential mediators.

METHODS

We used the mouse GLP-1-secreting cell line, GLUTag, and performed live cell imaging to examine the contribution of V1a and V1b vasopressin receptors (V1aR and V1bR, respectively) to GLP-1 secretion. We next investigated the hormone dynamics of V1aR-deficient mice (V1aR-/- mice), V1bR-deficient mice (V1bR-/- mice), and V1aR/V1bR-double deficient mice (V1aR-/-V1bR-/-mice).

RESULTS

AVP induced the increase in intracellular Ca2+ levels and GLP-1 secretion from GLUTag cells in a V1aR and V1bR-dependent manner. AVP receptor-deficient mice, particularly V1aR-/-V1bR-/- mice, demonstrated impaired secretion of GLP-1 and peptide YY secreted by enteroendocrine L cells. V1aR-/-V1bR-/-mice also exhibited abnormal lipid accumulation in the brown adipose tissue and skeletal muscle. We discovered that V1aR-/-V1bR-/- mice showed increased Paneth cell-related gene expression in the small intestine, which was attributed to increased fecal butyric acid levels. Exposure to butyric acid reduced GLP-1 secretion in L cell line. Additionally, human Paneth cell-related gene expressions negatively correlated with that of V1 receptor genes.

CONCLUSIONS

The deficiency in V1 receptor genes may increase gut butyric acid levels and impair the function of L cells, thus dysregulating lipid homeostasis in the brown adipose tissue and skeletal muscle. This study highlights the importance of appropriate control of the gut microbiota and its metabolites, including butyric acid, for the optimum functioning of enteroendocrine cells.

Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo

Recently, RNA velocity has driven a paradigmatic change in single-cell RNA sequencing (scRNA-seq) studies, allowing the reconstruction and prediction of directed trajectories in cell differentiation and state transitions. Most existing methods of dynamic modeling use ordinary differential equations (ODE) for individual genes without applying multivariate approaches. However, this modeling strategy inadequately captures the intrinsically stochastic nature of transcriptional dynamics governed by a cell-specific latent time across multiple genes, potentially leading to erroneous results. Here, we present SDEvelo, a generative approach to inferring RNA velocity by modeling the dynamics of unspliced and spliced RNAs via multivariate stochastic differential equations (SDE). Uniquely, SDEvelo explicitly models inherent uncertainty in transcriptional dynamics while estimating a cell-specific latent time across genes. Using both simulated and four scRNA-seq and spatial transcriptomics datasets, we show that SDEvelo can model the random dynamic patterns of mature-state cells while accurately detecting carcinogenesis. Additionally, the estimated gene-shared latent time can facilitate many downstream analyses for biological discovery. We demonstrate that SDEvelo is computationally scalable and applicable to both scRNA-seq and sequencing-based spatial transcriptomics data.

New insights into the role of mitophagy related gene affecting the metastasis of osteosarcoma through scRNA-seq and CRISPR-Cas9 genome editing

BACKGROUND

Osteosarcoma (OSA), the most common primary bone malignancy, poses significant challenges due to its aggressive nature and propensity for metastasis, especially in adolescents. Mitophagy analysis can help identify new therapeutic targets and combined treatment strategies.

METHODS

This study integrates single-cell sequencing (scRNA-seq) data and bulk-seq to identify mitophagy-related genes (MRGs) associated with the progression of OSA metastasis and analyze their clinical significance. scRNA-seq data elucidates the relationship between mitophagy and OSA metastasis, employing "CellChat" R package to explore intercellular communications and report on hundreds of ligand-receptor interactions. Subsequently, the combination of bulk-seq and CRISPR-Cas9 gene editing identifies mitophagy-related biomarker associated with metastatic prognosis. Finally, validation of the relationship between mitophagy and OSA metastasis is achieved through cellular biology experiments and animal studies.

RESULTS

The distinct mitophagy activity of various mitochondria manifests in diverse spatial localization, cellular developmental trajectories, and intercellular interactions. OSA tissue exhibits notable heterogeneity in mitophagy within osteoblastic OSA cells. However, high mitophagy activity correlates consistently with high metastatic potential. Subsequently, we identified three critical genes associated with mitophagy in OSA, namely RPS27A, TOMM20 and UBB. According to the aforementioned queue of genes, we have constructed a mitophagy_score (MIP_score). We observed that it consistently predicts patient prognosis in both internal and external datasets, demonstrating strong robustness and stability. Furthermore, we have found that MIP_score can also guide chemotherapy, with varying sensitivities to chemotherapeutic agents based on different MIP_score. It is noteworthy that, through the integration of CRISPR-Cas9 genome-wide screening and validation via cellular and animal experiments, we have identified RPS27A as a potential novel biomarker for OSA.

CONCLUSIONS

Our comprehensive analysis elucidated the profile of mitophagy throughout the OSA metastasis process, forming the basis for a mitophagy-related prognostic model that addresses clinical outcomes and drug sensitivity following OSA metastasis. Additionally, an online interactive platform was established to assist clinicians in decision-making ( https://mip-score.shinyapps.io/labtan/ ). These findings lay the groundwork for developing targeted therapies aimed at improving the prognosis of OSA patients.

VICTOR: Validation and inspection of cell type annotation through optimal regression

Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.

Airway-derived emphysema-specific alveolar type II cells exhibit impaired regenerative potential in COPD

Emphysema, the progressive destruction of gas exchange surfaces in the lungs, is a hallmark of COPD that is presently incurable. This therapeutic gap is largely due to a poor understanding of potential drivers of impaired tissue regeneration, such as abnormal lung epithelial progenitor cells, including alveolar type II (ATII) and airway club cells. We discovered an emphysema-specific subpopulation of ATII cells located in enlarged distal alveolar sacs, termed asATII cells. Single-cell RNA sequencing and in situ localisation revealed that asATII cells co-express the alveolar marker surfactant protein C and the club cell marker secretaglobin-3A2 (SCGB3A2). A similar ATII subpopulation derived from club cells was also identified in mouse COPD models using lineage labelling. Human and mouse ATII subpopulations formed 80-90% fewer alveolar organoids than healthy controls, indicating reduced progenitor function. Targeting asATII cells or their progenitor club cells could reveal novel COPD treatment strategies.

scQA: A dual-perspective cell type identification model for single cell transcriptome data

Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

Advancing toward a unified eosinophil signature from transcriptional profiling

Eosinophils are granulocytes that can accumulate in increased numbers in tissues and/or peripheral blood in disease. Phenotyping of eosinophils in health and disease has the potential to improve the precision of diagnosis and choice of therapies for eosinophilic-associated diseases. Transcriptional profiling of eosinophils has been plagued by cell fragility and difficulty isolating high-quality RNA. With several technological advances, single-cell RNA sequencing has become possible with eosinophils, at least from mice, while bulk RNA sequencing and microarrays have been performed in both murine and human samples. Anticipating more eosinophil transcriptional profiles in the coming years, we provide a summary of prior studies conducted on mouse and human eosinophils in blood and tissue, with a discussion of the advantages and potential pitfalls of various approaches. Common technical standards in studying eosinophil biology would help advance the field and make cross-study comparisons possible. Knowledge gaps and opportunities include identifying a minimal set of genes that define the eosinophil lineage, comparative studies between active disease and remission vs. homeostasis or development, especially in humans, and a comprehensive comparison between murine and human eosinophils at the transcriptional level. Characterizing such transcriptional patterns will be important to understanding the complex and diverse roles of eosinophils in both health and disease.

Single-cell sequencing of full-length transcripts and T-cell receptors with automated high-throughput Smart-seq3

We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow that integrates best practices and an optimized protocol to enhance efficiency, scalability, and method reproducibility. This workflow consistently produces high-quality data with high cell capture efficiency and gene detection sensitivity. In a rigorous comparison with the 10X platform using human primary CD4 + T-cells, HT Smart-seq3 demonstrated higher cell capture efficiency, greater gene detection sensitivity, and lower dropout rates. Additionally, when sufficiently scaled, HT Smart-seq3 achieved a comparable resolution of cellular heterogeneity to 10X. Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified a greater number of productive alpha and beta chain pairs without the need for additional primer design to amplify full-length V(D)J segments, enabling more comprehensive TCR profiling across a broader range of species. Taken together, HT Smart-seq3 overcomes key technical challenges, offering distinct advantages that position it as a promising solution for the characterization of single-cell transcriptomes and immune repertoires, particularly well-suited for low-input, low-RNA content samples.

Detection of >400 Cluster of Differentiation Biomarkers and Pathway Proteins in Single Immune Cells by Cyclic Multiplex In Situ Tagging for Single-Cell Proteomic Studies

The identification and characterization of immune cell subpopulations are critical to reveal cell development throughout life and immune responses to environmental factors. Next-generation sequencing technologies have dramatically advanced single-cell genomics and transcriptomics for immune cell classification. However, gene expression is often not correlated with protein expression, and immunotyping is mostly accepted in protein format. Current single-cell proteomic technologies are either limited in multiplex capacity or not sensitive enough to detect the critical functional proteins. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology to simultaneously measure >400 proteins, a scale of >10 times than similar technologies. Such an ultrahigh multiplexity is achieved by reiterative staining of the single cells coupled with a MIST array for detection. This technology has been thoroughly validated through comparison with flow cytometry and fluorescence immunostaining techniques. Both peripheral blood mononuclear cells (PBMCs) and T cells are analyzed by the CycMIST technology, and almost the entire spectrum of cluster of differentiation (CD) surface markers has been measured. The landscape of fluctuation of CD protein expression in single cells has been uncovered by our technology. Further study found T cell activation signatures and protein-protein networks. This study represents the highest multiplexity of single immune cell marker measurement targeting functional proteins. With additional information from intracellular proteins of the same single cells, our technology can potentially facilitate mechanistic studies of immune responses under various disease conditions.

Characterization and implementation of the MarathonRT template-switching reaction to expand the capabilities of RNA-seq

End-to-end RNA-sequencing methods that capture 5'-sequence content without cumbersome library manipulations are of great interest, particularly for analysis of long RNAs. While template-switching methods have been developed for RNA sequencing by distributive short-read RTs, such as the MMLV RTs used in SMART-Seq methods, they have not been adapted to leverage the power of ultraprocessive RTs, such as those derived from group II introns. To facilitate this transition, we dissected the individual processes that guide the enzymatic specificity and efficiency of the multistep template-switching reaction carried out by RTs, in this case, by MarathonRT. Remarkably, this is the first study of its kind, for any RT. First, we characterized the nucleotide specificity of nontemplated addition (NTA) reaction that occurs when the RT extends past the RNA 5'-terminus. We then evaluated the binding specificity of specialized template-switching oligonucleotides, optimizing their sequences and chemical properties to guide efficient template-switching reaction. Having dissected and optimized these individual steps, we then unified them into a procedure for performing RNA sequencing with MarathonRT enzymes, using a well-characterized RNA reference set. The resulting reads span a six-log range in transcript concentration and accurately represent the input RNA identities in both length and composition. We also performed RNA-seq from total human RNA and poly(A)-enriched RNA, with short- and long-read sequencing demonstrating that MarathonRT enhances the discovery of unseen RNA molecules by conventional RT. Altogether, we have generated a new pipeline for rapid, accurate sequencing of complex RNA libraries containing mixtures of long RNA transcripts.

Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations

Our ability to understand the cellular complexity of tissues has been revolutionized in recent years with significant advances in proteogenomic technologies including those enabling spatial analyses. This has led to numerous consortium efforts, such as the human cell atlas initiative which aims to profile all cells in the human body in healthy and diseased contexts. The availability of such information will subsequently lead to the identification of novel biomarkers of disease and of course therapeutic avenues. However, before such an atlas of any given healthy or diseased tissue can be generated, several factors should be considered including which specific techniques are optimal for the biological question at hand. In this review, we aim to highlight some of the considerations we believe to be important in the experimental design and analysis process, with the goal of helping to navigate the rapidly changing landscape of technologies available.

Single-cell transcriptomics reveals aberrant skin-resident cell populations and identifies fibroblasts as a determinant in rosacea

Rosacea is a chronic inflammatory skin disorder, whose underlying cellular and molecular mechanisms remain obscure. Here, we generate a single-cell atlas of facial skin from female rosacea patients and healthy individuals. Among keratinocytes, a subpopulation characterized by IFNγ-mediated barrier function damage is found to be unique to rosacea lesions. Blocking IFNγ signaling alleviates rosacea-like phenotypes and skin barrier damage in mice. The papulopustular rosacea is featured by expansion of pro-inflammatory fibroblasts, Schwann, endothelial and macrophage/dendritic cells. The frequencies of type 1/17 and skin-resident memory T cells are increased, and vascular mural cells are characterized by activation of inflammatory pathways and impaired muscle contraction function in rosacea. Most importantly, fibroblasts are identified as the leading cell type producing pro-inflammatory and vasodilative signals in rosacea. Depletion of fibroblasts or knockdown of PTGDS, a gene specifically upregulated in fibroblasts, blocks rosacea development in mice. Our study provides a comprehensive understanding of the aberrant alterations of skin-resident cell populations and identifies fibroblasts as a key determinant in rosacea development.

Assessing and mitigating batch effects in large-scale omics studies

Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

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

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

Cellular origin and clonal evolution of human dedifferentiated liposarcoma

Dedifferentiated liposarcoma (DDLPS) is the most frequent high-grade soft tissue sarcoma subtype. It is characterized by a component of undifferentiated tumor cells coexisting with a component of well-differentiated adipocytic tumor cells. Both dedifferentiated (DD) and well-differentiated (WD) components exhibit MDM2 amplification, however their cellular origin remains elusive. Using single-cell RNA sequencing, DNA sequencing, in situ multiplex immunofluorescence and functional assays in paired WD and DD components from primary DDLPS tumors, we characterize the cellular heterogeneity of DDLPS tumor and micro-environment. We identify a population of tumor adipocyte stem cells (ASC) showing striking similarities with adipocyte stromal progenitors found in white adipose tissue. We show that tumor ASC harbor the ancestral genomic alterations of WD and DD components, suggesting that both derive from these progenitors following clonal evolution. Last, we show that DD tumor cells keep important biological properties of ASC including pluripotency and that their adipogenic properties are inhibited by a TGF-β-high immunosuppressive tumor micro-environment.

Transcriptomic dysregulation and autistic-like behaviors in Kmt2c haploinsufficient mice rescued by an LSD1 inhibitor

Recent studies have consistently demonstrated that the regulation of chromatin and gene transcription plays a pivotal role in the pathogenesis of neurodevelopmental disorders. Among many genes involved in these pathways, KMT2C, encoding one of the six known histone H3 lysine 4 (H3K4) methyltransferases in humans and rodents, was identified as a gene whose heterozygous loss-of-function variants are causally associated with autism spectrum disorder (ASD) and the Kleefstra syndrome phenotypic spectrum. However, little is known about how KMT2C haploinsufficiency causes neurodevelopmental deficits and how these conditions can be treated. To address this, we developed and analyzed genetically engineered mice with a heterozygous frameshift mutation of Kmt2c (Kmt2c+/fs mice) as a disease model with high etiological validity. In a series of behavioral analyses, the mutant mice exhibit autistic-like behaviors such as impairments in sociality, flexibility, and working memory, demonstrating their face validity as an ASD model. To investigate the molecular basis of the observed abnormalities, we performed a transcriptomic analysis of their bulk adult brains and found that ASD risk genes were specifically enriched in the upregulated differentially expressed genes (DEGs), whereas KMT2C peaks detected by ChIP-seq were significantly co-localized with the downregulated genes, suggesting an important role of putative indirect effects of Kmt2c haploinsufficiency. We further performed single-cell RNA sequencing of newborn mouse brains to obtain cell type-resolved insights at an earlier stage. By integrating findings from ASD exome sequencing, genome-wide association, and postmortem brain studies to characterize DEGs in each cell cluster, we found strong ASD-associated transcriptomic changes in radial glia and immature neurons with no obvious bias toward upregulated or downregulated DEGs. On the other hand, there was no significant gross change in the cellular composition. Lastly, we explored potential therapeutic agents and demonstrate that vafidemstat, a lysine-specific histone demethylase 1 (LSD1) inhibitor that was effective in other models of neuropsychiatric/neurodevelopmental disorders, ameliorates impairments in sociality but not working memory in adult Kmt2c+/fs mice. Intriguingly, the administration of vafidemstat was shown to alter the vast majority of DEGs in the direction to normalize the transcriptomic abnormalities in the mutant mice (94.3 and 82.5% of the significant upregulated and downregulated DEGs, respectively, P < 2.2 × 10-16, binomial test), which could be the molecular mechanism underlying the behavioral rescuing. In summary, our study expands the repertoire of ASD models with high etiological and face validity, elucidates the cell-type resolved molecular alterations due to Kmt2c haploinsufficiency, and demonstrates the efficacy of an LSD1 inhibitor that might be generalizable to multiple categories of psychiatric disorders along with a better understanding of its presumed mechanisms of action.

Systematic comparison of sequencing-based spatial transcriptomic methods

Recent developments of sequencing-based spatial transcriptomics (sST) have catalyzed important advancements by facilitating transcriptome-scale spatial gene expression measurement. Despite this progress, efforts to comprehensively benchmark different platforms are currently lacking. The extant variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. In this study, we established a collection of reference tissues and regions characterized by well-defined histological architectures, and used them to generate data to compare 11 sST methods. We highlighted molecular diffusion as a variable parameter across different methods and tissues, significantly affecting the effective resolutions. Furthermore, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data, including an enhanced ability to capture patterned rare cell states along with specific markers, albeit being influenced by multiple factors including sequencing depth and resolution. Our study assists biologists in sST platform selection, and helps foster a consensus on evaluation standards and establish a framework for future benchmarking efforts that can be used as a gold standard for the development and benchmarking of computational tools for spatial transcriptomic analysis.

A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data

Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.

Emerging toolkits for decoding the co-occurrence of modified histones and chromatin proteins

In eukaryotes, DNA is packaged into chromatin with the help of highly conserved histone proteins. Together with DNA-binding proteins, posttranslational modifications (PTMs) on these histones play crucial roles in regulating genome function, cell fate determination, inheritance of acquired traits, cellular states, and diseases. While most studies have focused on individual DNA-binding proteins, chromatin proteins, or histone PTMs in bulk cell populations, such chromatin features co-occur and potentially act cooperatively to accomplish specific functions in a given cell. This review discusses state-of-the-art techniques for the simultaneous profiling of multiple chromatin features in low-input samples and single cells, focusing on histone PTMs, DNA-binding, and chromatin proteins. We cover the origins of the currently available toolkits, compare and contrast their characteristic features, and discuss challenges and perspectives for future applications. Studying the co-occurrence of histone PTMs, DNA-binding proteins, and chromatin proteins in single cells will be central for a better understanding of the biological relevance of combinatorial chromatin features, their impact on genomic output, and cellular heterogeneity.

Spatial transcriptomic profiling of isolated microregions in tissue sections utilizing laser-induced forward transfer

Profiling gene expression while preserving cell locations aids in the comprehensive understanding of cell fates in multicellular organisms. However, simple and flexible isolation of microregions of interest (mROIs) for spatial transcriptomics is still challenging. We present a laser-induced forward transfer (LIFT)-based method combined with a full-length mRNA-sequencing protocol (LIFT-seq) for profiling region-specific tissues. LIFT-seq demonstrated that mROIs from two adjacent sections could reliably and sensitively detect and display gene expression. In addition, LIFT-seq can identify region-specific mROIs in the mouse cortex and hippocampus. Finally, LIFT-seq identified marker genes in different layers of the cortex with very similar expression patterns. These genes were then validated using in situ hybridization (ISH) results. Therefore, LIFT-seq will be a valuable and efficient technique for profiling the spatial transcriptome in various tissues.

Cellular heterogeneity and key subsets of tissue-resident memory T cells in cervical cancer

Tissue-resident memory T cells (TRMs) play a critical role in cancer immunity by offering quick and effective immune responses. However, the cellular heterogeneity of TRMs and their significance in cervical cancer (CC) remain unknown. In this study, we generated and analyzed single-cell RNA sequencing data from 12,945 TRMs (ITGAE+ CD3D+) and 25,627 non-TRMs (ITGAE- CD3D+), derived from 11 CC tissues and 5 normal cervical tissues. We found that TRMs were more immunoreactive than non-TRMs, and TRMs in CC tissues were more activated than those in normal cervical tissues. Six CD8+ TRM subclusters and one CD4+ TRM subcluster were identified. Among them, CXCL13+ CD8+ TRMs were more abundant in CC tissues than in normal cervical tissues, had both cytotoxic and inhibitory features, and were enriched in pathways related to defense responses to the virus. Meanwhile, PLAC8+ CD8+ TRMs were less abundant in CC tissues than in normal cervical tissues but had highly cytotoxic features. The signature gene set scores of both cell subclusters were positively correlated with the overall survival and progression-free survival of patients with CC following radiotherapy. Of note, the association between HLA-E and NKG2A, either alone or in a complex with CD94, was enriched in CXCL13+ CD8+ TRMs interacting with epithelial cells at CC tissues. The in-depth characterization of TRMs heterogeneity in the microenvironment of CC could have important implications for advancing treatment and improving the prognosis of patients with CC.

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing

Single-cell sequencing is frequently affected by "omission" due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly "omitted" cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of "omitted" cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.

Single-cell total-RNA profiling unveils regulatory hubs of transcription factors

Recent development of RNA velocity uses master equations to establish the kinetics of the life cycle of RNAs from unspliced RNA to spliced RNA (i.e., mature RNA) to degradation. To feed this kinetic analysis, simultaneous measurement of unspliced RNA and spliced RNA in single cells is greatly desired. However, the majority of single-cell RNA-seq chemistry primarily captures mature RNA species to measure gene expressions. Here, we develop a one-step total-RNA chemistry-based single-cell RNA-seq method: snapTotal-seq. We benchmark this method with multiple single-cell RNA-seq assays in their performance in kinetic analysis of cell cycle by RNA velocity. Next, with LASSO regression between transcription factors, we identify the critical regulatory hubs mediating the cell cycle dynamics. We also apply snapTotal-seq to profile the oncogene-induced senescence and identify the key regulatory hubs governing the entry of senescence. Furthermore, from the comparative analysis of unspliced RNA and spliced RNA, we identify a significant portion of genes whose expression changes occur in spliced RNA but not to the same degree in unspliced RNA, indicating these gene expression changes are mainly controlled by post-transcriptional regulation. Overall, we demonstrate that snapTotal-seq can provide enriched information about gene regulation, especially during the transition between cell states.

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Identifying cell states in single-cell RNA-seq data at statistically maximal resolution

Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because of the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with 'similar' expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods. Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstates which operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters. We show that, on synthetic datasets, Cellstates almost perfectly recovers optimal partitions. On real data, Cellstates robustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstates identifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstates tool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important differentially expressed genes at each branch of this hierarchy, and to visualize these results.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.