Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods

Electrochemical and computational studies on the interaction between calf-thymus DNA and skin whitening agent arbutin

The interaction between double-stranded calf thymus DNA (ctDNA) and the skin whitening agent arbutin (AR) examined by applying electrochemical and computational methods for the first time in literature. A single-use pencil graphite electrode via cyclic (CV) and differential pulse voltammetry (DPV) techniques were applied to determine the kinetic and thermodynamic parameters in the absence and presence of ctDNA. To examine the interaction process, oxidation peak currents and potentials of AR were observed prior to the addition of various ctDNA concentrations. The binding constants (KAR-DNA) and Gibbs free energy (ΔG°) values for the AR-DNA complex were determined as 1.82 × 104L/mol and -24.30 kJ/mol at 298 K, respectively. Temperature evaluation of the interaction was examined using thermodynamic parameters (ΔH°: -30.30 kJ/mol and ΔS°: -0.00197 kJ/mol) applying the Van't Hoff equation. The local interaction sites in the molecule structure were determined by applying Fukui functions and second-order perturbation theory in view of potential hydrogen binding centers. The optimized structure of AR was applied with a DNA structure revealing the binding position for AR-DNA complex. Experimental and computational examinations suggested that AR-DNA binds to ctDNA through a minor groove mode via conventional hydrogen bonds, hydrophobic interactions and van der Waals forces.

Single-cell and spatial omics: exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

Advances and applications in single-cell and spatial genomics

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

A spatially resolved transcriptome landscape during thyroid cancer progression

Tumor microenvironment (TME) remodeling plays a pivotal role in thyroid cancer progression, yet its spatial dynamics remain unclear. In this study, we integrate spatial transcriptomics and single-cell RNA sequencing to map the TME architecture across para-tumor thyroid (PT) tissue, papillary thyroid cancer (PTC), locally advanced PTC (LPTC), and anaplastic thyroid carcinoma (ATC). Our integrative analysis reveals extensive molecular and cellular heterogeneity during thyroid cancer progression, enabling the identification of three distinct thyrocyte meta-clusters, including TG+IYG+ subpopulation in PT, HLA-DRB1+HLA-DRA+ subpopulation in early cancerous stages, and APOE+APOC1+ subpopulation in late-stage progression. We reveal stage-specific tumor leading edge remodeling and establish high-confidence cell-cell interactions, such as COL8A1-ITHB1 in PTC, LAMB2-ITGB4 in LPTC, and SERPINE1-PLAUR in ATC. Notably, both SERPINE1 expression level and SERPINE1+ fibroblast abundance correlate with malignant progression and prognosis. These findings provide a spatially resolved framework of TME remodeling, offering insights for thyroid cancer diagnosis and treatment.

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.

SCITUNA: single-cell data integration tool using network alignment

BACKGROUND

As single-cell genomics experiments increase in complexity and scale, the need to integrate multiple datasets has grown. Such integration enhances cellular feature identification by leveraging larger data volumes. However, batch effects-technical variations arising from differences in labs, times, or protocols-pose a significant challenge. Despite numerous proposed batch correction methods, many still have limitations, such as outputting only dimension-reduced data, relying on computationally intensive models, or resulting in overcorrection for batches with diverse cell type composition.

RESULTS

We introduce a novel method for batch effect correction named SCITUNA, a Single-Cell data Integration Tool Using Network Alignment. We perform evaluations on 39 individual batches from four real datasets and a simulated dataset, which include both scRNA-seq and scATAC-seq datasets, spanning multiple organisms and tissues. A thorough comparison of existing batch correction methods using 13 metrics reveals that SCITUNA outperforms current approaches and is successful at preserving biological signals present in the original data. In particular, SCITUNA shows a better performance than the current methods in all the comparisons except for the multiple batch integration of the lung dataset where the difference is 0.004.

CONCLUSION

SCITUNA effectively removes batch effects while retaining the biological signals present in the data. Our extensive experiments reveal that SCITUNA will be a valuable tool for diverse integration tasks.

Exploration of Conserved Human Adipose Subpopulations Using Targeted Single-Nuclei RNA Sequencing Data Sets

BACKGROUND

Smooth-muscle cells and pericytes are mural cells. Pericytes can differentiate into myofibroblasts, chondrocytes, vascular smooth-muscle cells, and adipocytes, marking them as a distinct progenitor population. Our goal was to molecularly define the progenitor cell populations in human adipose tissues and test the adipogenic potential of human mural cells.

METHODS

We used informatic analysis of single-cell RNA sequencing data from human tissues to identify and define pericytes and adipose progenitor cells found in human adipose tissues, including perivascular, brown, and white adipose tissues.

RESULTS

We established tissue-specific patterns of gene expression in pericytes and other putative human adipocyte progenitor cells. PPARG-expressing pericytes were present in multiple human adipose depots with consistent expression of COL25A1, MYO1B, and POSTN. We also found evidence of tissue-specific pericyte markers. Although there is some conservation between human and mouse adipose tissues, human pericyte populations have unique, depot-specific gene expression signatures. Immunofluorescence staining of human adipose tissue revealed the presence of pericytes both distant from and adjacent to vasculature in human adipose tissue. Additionally, we demonstrated the potential of human brain pericytes and aortic vascular smooth-muscle cells to differentiate into adipocytes in vitro on the basis of intracellular lipid accumulation and expression of adipocyte markers.

CONCLUSIONS

Human adipose cell populations are distinct from mice, and the pericyte subpopulation in human adipose tissues are present across adipose depots. Given that vascular mural cells, including pericytes and smooth-muscle cells, can undergo adipogenesis, we postulate that they are a novel source of adipocytes in the vascular microenvironment.

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

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

Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation

Processing biological data is a challenge of paramount importance as the amount of accumulated data has been annually increasing along with the emergence of new methods for studying biological objects. Blind application of mathematical methods in biology may lead to erroneous hypotheses and conclusions. Here we narrow our focus down to a small set of mathematical methods applied upon standard processing of scRNA-seq data: preprocessing, dimensionality reduction, integration, and clustering (using machine learning methods for clustering). Normalization and scaling are standard manipulations for the pre-processing with LogNormalize (natural-log transformation), CLR (centered log ratio transformation), and RC (relative counts) being employed as methods for data transformation. The justification for applying these methods in biology is not discussed in methodological articles. The essential aspect of dimensionality reduction is to identify the stable patterns which are deliberately removed upon mathematical data processing as being redundant, albeit containing important minor details for biological interpretation. There are no established rules for integration of datasets obtained at different sampling times or conditions. Clustering calls for reconsidering its application specifically for biological data processing. The novelty of the present study lies in an integrated approach of biology and bioinformatics to elucidate biological insights upon data processing.

Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research

In recent years, significant advancements in biochemistry, materials science, engineering, and computer-aided testing have driven the development of high-throughput tools for profiling genetic information. Single-cell RNA sequencing (scRNA-seq) technologies have established themselves as key tools for dissecting genetic sequences at the level of single cells. These technologies reveal cellular diversity and allow for the exploration of cell states and transformations with exceptional resolution. Unlike bulk sequencing, which provides population-averaged data, scRNA-seq can detect cell subtypes or gene expression variations that would otherwise be overlooked. However, a key limitation of scRNA-seq is its inability to preserve spatial information about the RNA transcriptome, as the process requires tissue dissociation and cell isolation. Spatial transcriptomics is a pivotal advancement in medical biotechnology, facilitating the identification of molecules such as RNA in their original spatial context within tissue sections at the single-cell level. This capability offers a substantial advantage over traditional single-cell sequencing techniques. Spatial transcriptomics offers valuable insights into a wide range of biomedical fields, including neurology, embryology, cancer research, immunology, and histology. This review highlights single-cell sequencing approaches, recent technological developments, associated challenges, various techniques for expression data analysis, and their applications in disciplines such as cancer research, microbiology, neuroscience, reproductive biology, and immunology. It highlights the critical role of single-cell sequencing tools in characterizing the dynamic nature of individual cells.

Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life

Cross-species single-cell RNA-seq data hold immense potential for unraveling cell type evolution and transferring knowledge between well-explored and less-studied species. However, challenges arise from interspecific genetic variation, batch effects stemming from experimental discrepancies and inherent individual biological differences. Here, we benchmarked nine data-integration methods across 20 species, encompassing 4.7 million cells, spanning eight phyla and the entire animal taxonomic hierarchy. Our evaluation reveals notable differences between the methods in removing batch effects and preserving biological variance across taxonomic distances. Methods that effectively leverage gene sequence information capture underlying biological variances, while generative model-based approaches excel in batch effect removal. SATURN demonstrates robust performance across diverse taxonomic levels, from cross-genus to cross-phylum, emphasizing its versatility. SAMap excels in integrating species beyond the cross-family level, especially for atlas-level cross-species integration, while scGen shines within or below the cross-class hierarchy. As a result, our analysis offers recommendations and guidelines for selecting suitable integration methods, enhancing cross-species single-cell RNA-seq analyses and advancing algorithm development.

Bgee in 2024: focus on curated single-cell RNA-seq datasets, and query tools

Bgee (https://www.bgee.org/) is a database to retrieve and compare gene expression patterns in multiple animal species. Expression data are integrated and made comparable between species thanks to consistent data annotation and processing. In the past years, we have integrated single-cell RNA-sequencing expression data into Bgee through careful curation of public datasets in multiple species. We have fully integrated this new technology along with the wealth of other data existing in Bgee. As a result, Bgee can now provide one definitive answer all the way to the cell resolution about a gene's expression pattern, comparable between species. We have updated our programmatic access tools to adapt to these changes accordingly. We have introduced a new web interface, providing detailed access to our annotations and expression data. It enables users to retrieve data, e.g. for specific organs, cell types or developmental stages, and leverages ontology reasoning to build powerful queries. Finally, we have expanded our species count from 29 to 52, emphasizing fish species critical for vertebrate genome studies, species of agronomic and veterinary importance and nonhuman primates.

Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach

Single-cell (sc) RNA, ATAC, and Multiome sequencing became powerful tools for uncovering biological and disease mechanisms. Unfortunately, manual analysis of sc data presents multiple challenges due to large data volumes and complexity of configuration parameters. This complexity, as well as not being able to reproduce a computational environment, affects the reproducibility of analysis results. The Scientific Data Analysis Platform ( https://SciDAP.com ) allows biologists without computational expertise to analyze sequencing-based data using portable and reproducible pipelines written in Common Workflow Language (CWL). Our suite of computational pipelines addresses the most common needs in scRNA-Seq, scATAC-Seq and scMultiome data analysis. When executed on SciDAP, it offers a user-friendly alternative to manual data processing, eliminating the need for coding expertise. In this protocol, we describe the use of SciDAP to analyze scMultiome data. Similar approaches can be used for analysis of scRNA-Seq, scATAC-Seq and scVDJ-Seq datasets.

Pinpointing the integration of artificial intelligence in liver cancer immune microenvironment

Liver cancer remains one of the most formidable challenges in modern medicine, characterized by its high incidence and mortality rate. Emerging evidence underscores the critical roles of the immune microenvironment in tumor initiation, development, prognosis, and therapeutic responsiveness. However, the composition of the immune microenvironment of liver cancer (LC-IME) and its association with clinicopathological significance remain unelucidated. In this review, we present the recent developments related to the use of artificial intelligence (AI) for studying the immune microenvironment of liver cancer, focusing on the deciphering of complex high-throughput data. Additionally, we discussed the current challenges of data harmonization and algorithm interpretability for studying LC-IME.

Deep feature batch correction using ComBat for machine learning applications in computational pathology

Background

Developing artificial intelligence (AI) models for digital pathology requires large datasets from multiple sources. However, without careful implementation, AI models risk learning confounding site-specific features in datasets instead of clinically relevant information, leading to overestimated performance, poor generalizability to real-world data, and potential misdiagnosis.

Methods

Whole-slide images (WSIs) from The Cancer Genome Atlas (TCGA) colon (COAD), and stomach adenocarcinoma datasets were selected for inclusion in this study. Patch embeddings were obtained using three feature extraction models, followed by ComBat harmonization. Attention-based multiple instance learning models were trained to predict tissue-source site (TSS), as well as clinical and genetic attributes, using raw, Macenko normalized, and Combat-harmonized patch embeddings.

Results

TSS prediction achieved high accuracy (AUROC > 0.95) with all three feature extraction models. ComBat harmonization significantly reduced the AUROC for TSS prediction, with mean AUROCs dropping to approximately 0.5 for most models, indicating successful mitigation of batch effects (e.g., CCL-ResNet50 in TCGA-COAD: Pre-ComBat AUROC = 0.960, Post-ComBat AUROC = 0.506, p < 0.001). Clinical attributes associated with TSS, such as race and treatment response, showed decreased predictability post-harmonization. Notably, the prediction of genetic features like MSI status remained robust after harmonization (e.g., MSI in TCGA-COAD: Pre-ComBat AUROC = 0.667, Post-ComBat AUROC = 0.669, p=0.952), indicating the preservation of true histological signals.

Conclusion

ComBat harmonization of deep learning-derived histology features effectively reduces the risk of AI models learning confounding features in WSIs, ensuring more reliable performance estimates. This approach is promising for the integration of large-scale digital pathology datasets.

Automated single-cell omics end-to-end framework with data-driven batch inference

To facilitate single-cell multi-omics analysis and improve reproducibility, we present single-cell pipeline for end-to-end data integration (SPEEDI), a fully automated end-to-end framework for batch inference, data integration, and cell-type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell-type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch-inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/. A record of this paper's transparent peer review process is included in the supplemental information.

A Strategy to Compare Single-Cell RNA Sequencing Data Sets Provides Phenotypic Insight into Cellular Heterogeneity Underlying Biological Similarities and Differences Between Samples

Single-cell RNA sequencing (scRNA-seq) allows for an unbiased assessment of cellular phenotypes by enabling the extraction of transcriptomic data. An important question in downstream analysis is how to evaluate biological similarities and differences between samples in high dimensional space. This becomes especially complex when there is cellular heterogeneity within the samples. Here, we present scCompare, a computational pipeline for comparison of scRNA-seq data sets. Phenotypic identities from a known data set are transferred onto another data set using correlation-based mapping to average transcriptomic signatures from each cluster of cells' annotated phenotype. Statistically derived lower cutoffs for phenotype inclusivity allow for cells to be unmapped if they are distinct from the known phenotypes, facilitating potential novel cell type detection. In a comparison of our tool using scRNA-seq data sets from human peripheral blood mononuclear cells (PBMCs), we show that scCompare outperforms single-cell variational inference (scVI) in higher precision and sensitivity for most of the cell types. scCompare was used on a cardiomyocyte data set where it confirmed the discovery of a distinct cluster of cells that differed between the 2 protocols for differentiation. Further use of scCompare on cell atlas data sets revealed insights into the cellular heterogeneity underpinning biological diversity between samples. In addition, we used a cell atlas to better understand the effect of key parameters used in the scCompare pipeline. We envision that scCompare will be of value to the research community when comparing large scRNA-seq data sets.

Microtubule function and dysfunction in the nervous system

Microtubules are core components of the neuronal cytoskeleton, providing structural support for the complex cytoarchitecture of neurons and serving as tracks for long-distance transport. The properties and functions of neuronal microtubules are controlled by tubulin isoforms and a variety of post-translational modifications, collectively known as the "tubulin code." The tubulin code exerts direct control over the intrinsic properties of neuronal microtubules and regulates the repertoire of proteins that read the code, which in turn, has a significant impact on microtubule stability and dynamics. Here, we review progress in the understanding of the tubulin code in the nervous system, with a particular focus on tubulin post-translational modifications that have been proposed as potential contributors to the development and maintenance of the mammalian nervous system. Furthermore, we also discuss the potential links between disruptions in the tubulin code and neurological disorders, including neurodevelopmental abnormalities and neurodegenerative diseases.

Erythropoietin-dependent Acquisition of CD71 hi CD105 hi Phenotype within CD235a - Early Erythroid Progenitors

The development of committed erythroid progenitors and their continued maturation into mature erythrocytes requires the cytokine erythropoietin (Epo). Here, we describe the immunophenotypic identification of a unique Epo-dependent colony-forming unit-erythroid (CFU-E) cell subtype that forms during early erythropoiesis (EE). This previously undescribed CFU-E subtype, termed late-CFU-E (lateC), lacks surface expression of the characteristic erythroid marker CD235a (glycophorin A) but has high levels of CD71 and CD105. LateCs could be prospectively detected in human bone marrow (BM) cells and, upon isolation and reculture, exhibited the potential to form CFU-E colonies in medium containing only Epo (no other cytokines) and continued differentiation along the erythroid trajectory. Analysis of ex vivo cultures of BM CD34 + cells showed that acquisition of the CD7 hi CD105 hi phenotype in lateCs is gradual and occurs through the formation of four EE cell subtypes. Of these, two are CD34 + burst-forming unit-erythroid (BFU-E) cells, distinguishable as CD7 lo CD105 lo early BFU-E and CD7 hi CD105 lo late BFU-E, and two are CD34 - CFU-Es, also distinguishable as CD71 lo CD105 lo early CFU-E and CD7 hi CD105 lo mid-CFU-E. The transition of these EE populations is accompanied by a rise in CD36 expression, such that all lateCs are CD36 + . Single cell RNA-sequencing analysis confirmed Epo-dependent formation of a CFU-E cluster that exhibits high coexpression of CD71, CD105, and CD36 transcripts. Gene set enrichment analysis revealed the involvement of genes specific to fatty acid and cholesterol metabolism in lateC formation. Overall, in addition to identifying a key Epo-dependent EE cell stage, this study provides a framework for investigation into mechanisms underlying other erythropoiesis-stimulating agents.

SCIntRuler: guiding the integration of multiple single-cell RNA-seq datasets with a novel statistical metric

MOTIVATION

The growing number of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets, such as augmenting sample sizes and enhancing analytical robustness. Inherent diversity and batch discrepancies within samples or across studies continue to pose significant challenges for computational analyses. Questions persist in practice, lacking definitive answers: Should we use a specific integration method or opt for simply merging the datasets during joint analysis? Among all the existing data integration methods, which one is more suitable in specific scenarios?

RESULT

To fill the gap, we introduce SCIntRuler, a novel statistical metric for guiding the integration of multiple scRNA-seq datasets. SCIntRuler helps researchers make informed decisions regarding the necessity of data integration and the selection of an appropriate integration method. Our simulations and real data applications demonstrate that SCIntRuler streamlines decision-making processes and facilitates the analysis of diverse scRNA-seq datasets under varying contexts, thereby alleviating the complexities associated with the integration of heterogeneous scRNA-seq datasets.

AVAILABILITY AND IMPLEMENTATION

The implementation of our method is available on CRAN as an open-source R package with a user-friendly manual available: https://cloud.r-project.org/web/packages/SCIntRuler/index.html.

Comprehensive review on single-cell RNA sequencing: A new frontier in Alzheimer's disease research

Alzheimer's disease (AD) is a multifaceted neurodegenerative condition marked by gradual cognitive deterioration and the loss of neurons. While conventional bulk RNA sequencing techniques have shed light on AD pathology, they frequently obscure the cellular diversity within brain tissues. The advent of single-cell RNA sequencing (scRNA-seq) has transformed our capability to analyze the cellular composition of AD, allowing for the detection of unique cell populations, rare cell types, and gene expression alterations at an individual cell level. This review examines the use of scRNA-seq in AD research, focusing on its contributions to understanding cellular diversity, disease progression, and potential therapeutic targets. We discuss key technological innovations, data analysis techniques, and challenges associated with scRNA-seq in studying AD. Furthermore, we highlight recent studies that have utilized scRNA-seq to identify novel biomarkers, uncover disease-associated pathways, and elucidate the role of non-neuronal cells, such as microglia and astrocytes, in AD pathogenesis. By providing a comprehensive overview of advancements in scRNA-seq for unraveling cellular heterogeneity in AD, this review highlights the transformative impact of scRNA-seq on our comprehension of disease mechanisms and the creation of targeted treatments.

ClusterMatch aligns single-cell RNA-sequencing data at the multi-scale cluster level via stable matching

MOTIVATION

Unsupervised clustering of single-cell RNA sequencing (scRNA-seq) data holds the promise of characterizing known and novel cell type in various biological and clinical contexts. However, intrinsic multi-scale clustering resolutions poses challenges to deal with multiple sources of variability in the high-dimensional and noisy data.

RESULTS

We present ClusterMatch, a stable match optimization model to align scRNA-seq data at the cluster level. In one hand, ClusterMatch leverages the mutual correspondence by canonical correlation analysis and multi-scale Louvain clustering algorithms to identify cluster with optimized resolutions. In the other hand, it utilizes stable matching framework to align scRNA-seq data in the latent space while maintaining interpretability with overlapped marker gene set. Through extensive experiments, we demonstrate the efficacy of ClusterMatch in data integration, cell type annotation, and cross-species/timepoint alignment scenarios. Our results show ClusterMatch's ability to utilize both global and local information of scRNA-seq data, sets the appropriate resolution of multi-scale clustering, and offers interpretability by utilizing marker genes.

AVAILABILITY AND IMPLEMENTATION

The code of ClusterMatch software is freely available at https://github.com/AMSSwanglab/ClusterMatch.

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.

Integrated single cell-RNA sequencing and Mendelian randomization for ischemic stroke and metabolic syndrome

Although more and more evidence has supported that metabolic syndrome (MS) is linked to ischemic stroke (IS), the molecular mechanism and genetic association between them has not been investigated. Here, we combined the existing single-cell RNA sequencing (scRNA-seq) data and mendelian randomization (MR) for stroke to understand the role of dysregulated metabolism in stroke. The shared hub genes were identified with machine learning and WGCNA. A total of six upregulated DEGs and five downregulated genes were selected for subsequent analyses. Nine genes were finally identified with random forest, Lasso regression, and XGBoost method as a potential diagnostic model. scRNA-seq also show the abnormal glycolysis level in most cell clusters in stroke and associated with the expression level of hub genes. The genetic relationship between IS and MS was verified with MR analysis. Our study reveals the common molecular profile and genetic association between ischemic stroke and metabolic syndrome.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, Pinnacle learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. Pinnacle's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. Pinnacle outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. Pinnacle's ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome

Tumor-associated myeloid-derived cells (MDCs) significantly impact cancer prognosis and treatment responses due to their remarkable plasticity and tumorigenic behaviors. Here, we integrate single-cell RNA-sequencing data from different cancer types, identifying 29 MDC subpopulations within the tumor microenvironment. Our analysis reveals abnormally expanded MDC subpopulations across various tumors and distinguishes cell states that have often been grouped together, such as TREM2+ and FOLR2+ subpopulations. Using deconvolution approaches, we identify five subpopulations as independent prognostic markers, including states co-expressing TREM2 and PD-1, and FOLR2 and PDL-2. Additionally, TREM2 alone does not reliably predict cancer prognosis, as other TREM2+ macrophages show varied associations with prognosis depending on local cues. Validation in independent cohorts confirms that FOLR2-expressing macrophages correlate with poor clinical outcomes in ovarian and triple-negative breast cancers. This comprehensive MDC atlas offers valuable insights and a foundation for futher analyses, advancing strategies for treating solid cancers.

PARE: A framework for removal of confounding effects from any distance-based dimension reduction method

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

Combinatorial transcriptomic and genetic dissection of insulin/IGF-1 signaling-regulated longevity in Caenorhabditis elegans

Classical genetic analysis is invaluable for understanding the genetic interactions underlying specific phenotypes, but requires laborious and subjective experiments to characterize polygenic and quantitative traits. Contrarily, transcriptomic analysis enables the simultaneous and objective identification of multiple genes whose expression changes are associated with specific phenotypes. Here, we conducted transcriptomic analysis of genes crucial for longevity using datasets with daf-2/insulin/IGF-1 receptor mutant Caenorhabditis elegans. Our analysis unraveled multiple epistatic relationships at the transcriptomic level, in addition to verifying genetically established interactions. Our combinatorial analysis also revealed transcriptomic changes associated with longevity conferred by daf-2 mutations. In particular, we demonstrated that the extent of lifespan changes caused by various mutant alleles of the longevity transcription factor daf-16/FOXO matched their effects on transcriptomic changes in daf-2 mutants. We identified specific aging-regulating signaling pathways and subsets of structural and functional RNA elements altered by different genes in daf-2 mutants. Lastly, we elucidated the functional cooperation between several longevity regulators, based on the combination of transcriptomic and molecular genetic analysis. These data suggest that different biological processes coordinately exert their effects on longevity in biological networks. Together our work demonstrates the utility of transcriptomic dissection analysis for identifying important genetic interactions for physiological processes, including aging and longevity.

Automated single-cell omics end-to-end framework with data-driven batch inference

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

Leveraging Multi-Tissue, Single-Cell Atlases as Tools to Elucidate Shared Mechanisms of Immune-Mediated Inflammatory Diseases

The observation that certain therapeutic strategies for targeting inflammation benefit patients with distinct immune-mediated inflammatory diseases (IMIDs) is exemplified by the success of TNF blockade in conditions including rheumatoid arthritis, ulcerative colitis, and skin psoriasis, albeit only for subsets of individuals with each condition. This suggests intersecting "nodes" in inflammatory networks at a molecular and cellular level may drive and/or maintain IMIDs, being "shared" between traditionally distinct diagnoses without mapping neatly to a single clinical phenotype. In line with this proposition, integrative tumour tissue analyses in oncology have highlighted novel cell states acting across diverse cancers, with important implications for precision medicine. Drawing upon advances in the oncology field, this narrative review will first summarise learnings from the Human Cell Atlas in health as a platform for interrogating IMID tissues. It will then review cross-disease studies to date that inform this endeavour before considering future directions in the field.

Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach

Single-cell (sc) RNA, ATAC and Multiome sequencing became powerful tools for uncovering biological and disease mechanisms. Unfortunately, manual analysis of sc data presents multiple challenges due to large data volumes and complexity of configuration parameters. This complexity, as well as not being able to reproduce a computational environment, affects the reproducibility of analysis results. The Scientific Data Analysis Platform (https://SciDAP.com) allows biologists without computational expertise to analyze sequencing-based data using portable and reproducible pipelines written in Common Workflow Language (CWL). Our suite of computational pipelines addresses the most common needs in scRNA-Seq, scATAC-Seq and scMultiome data analysis. When executed on SciDAP, it offers a user-friendly alternative to manual data processing, eliminating the need for coding expertise. In this protocol, we describe the use of SciDAP to analyze scMultiome data. Similar approaches can be used for analysis of scRNA-Seq, scATAC-Seq and scVDJ-Seq datasets.

Brief guide to RNA sequencing analysis for nonexperts in bioinformatics

Transcriptome analysis is widely used for current biological research but remains challenging for many experimental scientists. Here, we present a brief but broad guideline for transcriptome analysis, focusing on RNA sequencing, by providing the list of publicly available datasets, tools, and R packages for practical transcriptome analysis. This work will be useful for biologists to perform key transcriptomic analysis with minimum expertise in bioinformatics.

Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors (TFs) in intricate regulatory networks in a cell-type specific manner. Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings. This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets, addressing batch effects and conserving biological variance. This integration spans a broad spectrum of tissues, including both below- and above-ground parts. Utilizing a rigorous approach for cell type annotation, we identified 47 distinct cell types or states, largely expanding our current view of plant cell compositions. We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression. Taken together, our study not only offers extensive plant cell atlas exploration that serves as a valuable resource, but also provides molecular insights into gene-regulatory programs that varies from different cell types.

Meta-analysis of the transcriptome identifies aberrant RNA processing as common feature of aging in multiple species

Aging is accompanied by the gradual deregulation of the transcriptome. However, whether age-dependent changes in the transcriptome are evolutionarily conserved or diverged remains largely unexplored. Here, we performed a meta-analysis examining the age-dependent changes in the transcriptome using publicly available datasets of 11 representative metazoans, ranging from Caenorhabditis elegans to humans. To identify the transcriptomic changes associated with aging, we analyzed various aspects of the transcriptome, including genome composition, RNA processing, and functional consequences. The use of introns and novel splice sites tended to increase with age, particularly in the brain. In addition, our analysis suggests that the age-dependent accumulation of premature termination codon-containing transcripts is a common feature of aging across multiple animal species. Using C. elegans as a test model, we showed that several splicing factors that are evolutionarily conserved and age-dependently downregulated were required to maintain a normal lifespan. Thus, aberrant RNA processing appears to be associated with aging and a short lifespan in various species.

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations is challenging and requires stable and interpretable methods. However, the current cell type identification methods have limited performance, mainly due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, a multi-view graph convolutional network model (scMGCN) that integrates multiple graph structures from raw scRNA-seq data and applies graph convolutional networks with attention mechanisms to learn cell embeddings and predict cell labels. We evaluate our model on single-dataset, cross-species, and cross-platform experiments and compare it with other state-of-the-art methods. Our results show that scMGCN outperforms the other methods regarding stability, accuracy, and robustness to batch effects. Our main contributions are as follows: Firstly, we introduce multi-view learning and multiple graph construction methods to capture comprehensive cellular information from scRNA-seq data. Secondly, we construct a scMGCN that combines graph convolutional networks with attention mechanisms to extract shared, high-order information from cells. Finally, we demonstrate the effectiveness and superiority of the scMGCN on various datasets.

Integrating single-cell RNA-seq datasets with substantial batch effects

Integration of single-cell RNA-sequencing (scRNA-seq) datasets has become a standard part of the analysis, with conditional variational autoencoders (cVAE) being among the most popular approaches. Increasingly, researchers are asking to map cells across challenging cases such as cross-organs, species, or organoids and primary tissue, as well as different scRNA-seq protocols, including single-cell and single-nuclei. Current computational methods struggle to harmonize datasets with such substantial differences, driven by technical or biological variation. Here, we propose to address these challenges for the popular cVAE-based approaches by introducing and comparing a series of regularization constraints. The two commonly used strategies for increasing batch correction in cVAEs, that is Kullback-Leibler divergence (KL) regularization strength tuning and adversarial learning, suffer from substantial loss of biological information. Therefore, we adapt, implement, and assess alternative regularization strategies for cVAEs and investigate how they improve batch effect removal or better preserve biological variation, enabling us to propose an optimal cVAE-based integration strategy for complex systems. We show that using a VampPrior instead of the commonly used Gaussian prior not only improves the preservation of biological variation but also unexpectedly batch correction. Moreover, we show that our implementation of cycle-consistency loss leads to significantly better biological preservation than adversarial learning implemented in the previously proposed GLUE model. Additionally, we do not recommend relying only on the KL regularization strength tuning for increasing batch correction, as it removes both biological and batch information without discriminating between the two. Based on our findings, we propose a new model that combines VampPrior and cycle-consistency loss. We show that using it for datasets with substantial batch effects improves downstream interpretation of cell states and biological conditions. To ease the use of the newly proposed model, we make it available in the scvi-tools package as an external model named sysVI. Moreover, in the future, these regularization techniques could be added to other established cVAE-based models to improve the integration of datasets with substantial batch effects.

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.

A Roadmap for the Human Gut Cell Atlas

The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.

Transcriptomic Approaches in Studies on and Applications of Chimeric Antigen Receptor T Cells

Chimeric antigen receptor T (CAR-T) cells are specifically modified T cells which bear recombinant receptors, present at the cell surface and devoted to detect selected antigens of cancer cells, and due to the presence of transmembrane and activation domains, able to eliminate the latter ones. The use of CAR-T cells in anti-cancer therapies is a relatively novel approach, providing a powerful tool in the fight against cancer and bringing new hope for patients. However, despite huge possibilities and promising results of preclinical studies and clinical efficacy, there are various drawbacks to this therapy, including toxicity, possible relapses, restrictions to specific kinds of cancers, and others. Studies desiring to overcome these problems include various modern and advanced methods. One of them is transcriptomics, a set of techniques that analyze the abundance of all RNA transcripts present in the cell at certain moment and under certain conditions. The use of this method gives a global picture of the efficiency of expression of all genes, thus revealing the physiological state and regulatory processes occurring in the investigated cells. In this review, we summarize and discuss the use of transcriptomics in studies on and applications of CAR-T cells, especially in approaches focused on improved efficacy, reduced toxicity, new target cancers (like solid tumors), monitoring the treatment efficacy, developing novel analytical methods, and others.