CHAI: consensus clustering through similarity matrix integration for cell-type identification.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state-of-the-art clustering methods: CHAI-AvgSim and CHAI-SNF. CHAI-AvgSim and CHAI-SNF demonstrate superior performance across several benchmarking datasets. Furthermore, both CHAI methods outperform the most recent consensus clustering method, SAME-clustering. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI overcomes previous limitations by incorporating the most recent and top performing scRNAseq clustering algorithms into the aggregation framework. It is also an intuitive and easily customizable R package where users may add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. This ensures that as more advanced clustering algorithms are developed, CHAI will remain useful to the community as a generalized framework. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.

A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset.

Cell-type annotation is a critical step in single-cell data analysis. With the development of numerous cell annotation methods, it is necessary to evaluate these methods to help researchers use them effectively. Reference datasets are essential for evaluation, but currently, the cell labels of reference datasets mainly come from computational methods, which may have computational biases and may not reflect the actual cell-type outcomes. This study first constructed an experimentally labeled immune cell-subtype single-cell dataset of the same batch and systematically evaluated 18 cell annotation methods. We assessed those methods under five scenarios, including intra-dataset validation, immune cell-subtype validation, unsupervised clustering, inter-dataset annotation, and unknown cell-type prediction. Accuracy and ARI were evaluation metrics. The results showed that SVM, scBERT, and scDeepSort were the best-performing supervised methods. Seurat was the best-performing unsupervised clustering method, but it couldn't fully fit the actual cell-type distribution. Our results indicated that experimentally labeled immune cell-subtype datasets revealed the deficiencies of unsupervised clustering methods and provided new dataset support for supervised methods.

Correlation Between Near-Infrared Autofluorescence Properties and Sestamibi Uptakes of Parathyroid Glands in Primary Hyperparathyroidism.

OBJECTIVE: Near-infrared autofluorescence (NIRAF) characteristics of parathyroid glands in primary hyperparathyroidism (pHPT) vary, with unclarity regarding the underlying mechanism. Similarly, 99mTc-sestamibi uptake in diseased parathyroid glands is variable. There is a suggestion that oxyphilic cell content may influence both imaging modalities. This study aims to analyze the relationship between NIRAF imaging characteristics, 99mTc-sestamibi uptake, and cellular composition in pHPT. STUDY DESIGN: Retrospective analysis of an Institutional Review Board-monitored prospective database. SETTING: Single tertiary referral center. METHODS: NIRAF characteristics of parathyroid glands of patients with pHPT between 2019 and 2024 were compared with 99mTc-sestamibi scan findings from a prospective database. Using third-party software, brightness intensity and heterogeneity index (HI) of the glands were calculated. A subgroup of parathyroid glands obtained from consecutive patients with pHPT in 2020 to 2021 underwent histological analysis. RESULTS: A total of 428 patients with 638 diseased parathyroid glands were analyzed. Forty-seven percent of the glands showed an uptake on 99mTc-sestamibi scans. The brightness intensity of the NIRAF signals from parathyroid glands that were seen versus not seen on sestamibi was 2.1 versus 2.3 (P = .002) and HI 0.18 versus 0.17 (P = .35), respectively. On multivariate analysis, low autofluorescence intensity, high gland volume, and single adenoma were associated with detectability on 99mTc-sestamibi scan (P < .0001). Intraglandular adipose tissue content was lower in diseased glands that were detected on 99mTc-sestamibi scans (0% vs 5%, P < .0001). CONCLUSION: Our findings indicate an inverse relationship between autofluorescence intensity and detectability on 99mTc-sestamibi scans and a lack of correlation between different cell types and autofluorescence properties.

STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks.

Spatially resolved transcriptomics integrates high-throughput transcriptome measurements with preserved spatial cellular organization information. However, many technologies cannot reach single-cell resolution. We present STdGCN, a graph model leveraging single-cell RNA sequencing (scRNA-seq) as reference for cell-type deconvolution in spatial transcriptomic (ST) data. STdGCN incorporates expression profiles from scRNA-seq and spatial localization from ST data for deconvolution. Extensive benchmarking on multiple datasets demonstrates that STdGCN outperforms 17 state-of-the-art models. In a human breast cancer Visium dataset, STdGCN delineates stroma, lymphocytes, and cancer cells, aiding tumor microenvironment analysis. In human heart ST data, STdGCN identifies changes in endothelial-cardiomyocyte communications during tissue development.

The response to influenza vaccination is associated with DNA methylation-driven regulation of T cell innate antiviral pathways.

BACKGROUND: The effect of vaccination on the epigenome remains poorly characterized. In previous research, we identified an association between seroprotection against influenza and DNA methylation at sites associated with the RIG-1 signaling pathway, which recognizes viral double-stranded RNA and leads to a type I interferon response. However, these studies did not fully account for confounding factors including age, gender, and BMI, along with changes in cell-type composition. RESULTS: Here, we studied the influenza vaccine response in a longitudinal cohort vaccinated over two consecutive years (2019-2020 and 2020-2021), using peripheral blood mononuclear cells and a targeted DNA methylation approach. To address the effects of multiple factors on the epigenome, we designed a multivariate multiple regression model that included seroprotection levels as quantified by the hemagglutination-inhibition (HAI) assay test. CONCLUSIONS: Our findings indicate that 179 methylation sites can be combined as potential signatures to predict seroprotection. These sites were not only enriched for genes involved in the regulation of the RIG-I signaling pathway, as found previously, but also enriched for other genes associated with innate immunity to viruses and the transcription factor binding sites of BRD4, which is known to impact T cell memory. We propose a model to suggest that the RIG-I pathway and BRD4 could potentially be modulated to improve immunization strategies.

Cold mediates maize root hair developmental plasticity via epidermis-specific transcriptomic responses.

Cold stress during early development limits maize (Zea mays L.) production in temperate zones. Low temperatures restrict root growth and reprogram gene expression. Here, we provide a systematic transcriptomic landscape of maize primary roots, their tissues, and cell types in response to cold stress. The epidermis exhibited a unique transcriptomic cold response, and genes involved in root hair formation were dynamically regulated in this cell type by cold. Consequently, activation of genes involved in root hair tip growth contributed to root hair recovery under moderate cold conditions. The maize root hair defective mutants roothair defective 5 (rth5) and roothair defective 6 (rth6) displayed enhanced cold tolerance with respect to primary root elongation. Furthermore, dehydration response element-binding protein 2.1 (dreb2.1) was the only member of the dreb subfamily of AP2/EREB transcription factor genes upregulated in primary root tissues and cell types but exclusively downregulated in root hairs upon cold stress. Plants overexpressing dreb2.1 significantly suppressed root hair elongation after moderate cold stress. Finally, the expression of rth3 was regulated by dreb2.1 under cold conditions, while rth6 transcription was regulated by dreb2.1 irrespective of the temperature regime. We demonstrated that dreb2.1 negatively regulates root hair plasticity at low temperatures by coordinating the expression of root hair defective genes in maize.

XgCPred: Cell type classification using XGBoost-CNN integration and exploiting gene expression imaging in single-cell RNAseq data.

BACKGROUND: The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic research by enabling the exploration of gene expression at an individual cell level. This advancement sheds light on how cells differentiate and evolve over time. Effectively classification cell types within scRNA-seq datasets are essential for understanding the intricate cell compositions within tissues and elucidating the origins of various diseases. Challenges persist in the field, emphasizing the need for precise categorization across diverse datasets, addressing aggregated cell data, and managing the complexity of high-dimensional data spaces. METHODOLOGY: XgCPred is a novel approach combining XGBoost with convolutional neural networks (CNNs) to provide cell type classification with better accuracy in single-cell RNA-seq data. This combo reveals how well CNNs can detect spatial hierarchy in gene expression images and how XGBoost performs with large volumes of data. XgCPred utilizes an imaging representation of gene expression that is based on the hierarchical organization of genes found in the KEGG BRITE database. RESULTS: Rigorous testing of XgCPred across multiple scRNA-seq datasets, each presenting unique challenges such as varying cell counts, gene expression diversity, and cellular heterogeneity, has demonstrated its superiority compared to earlier methods. The algorithm shows remarkable accuracy and precision in cell type annotation, achieving near-perfect classification scores in some cases. These results underscore its capability to effectively manage data variability. CONCLUSIONS: XgCPred distinguishes itself through its dependable and accurate cell type classification across a range of scRNA-seq datasets. Its effectiveness stems from sophisticated data handling and its ability to adapt to the complexities inherent in scRNA-seq data. XgCPred delivers reliable cell annotations essential for further biological analysis and research, marking a significant advancement in genomic studies. With scRNA-seq datasets growing in size and complexity, XgCPred offers a scalable and potent solution for cell type identification, potentially enhancing our understanding of cellular biology and aiding in the precise detection of diseases. XgCPred is a useful tool in genomic research and tailored therapy because it solves current constraints on computing efficiency and generalizability.

Roles of naïve CD4+ T cells and their differentiated subtypes in lung adenocarcinoma and underlying potential regulatory pathways.

BACKGROUND: Naïve CD4+ T cells and their differentiated counterparts play a significant regulatory role in the tumor immune microenvironment, yet their effects on lung adenocarcinoma (LUAD) are not fully understood. METHODS: We utilized Mendelian randomization to assess the causal association between naïve CD4+ T cells and LUAD. Employing a modified single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm with The Cancer Genome Atlas (TCGA) database, we determined the infiltration levels of naïve CD4+ T cells and their differentiation subtypes and investigated their correlation with clinical characteristics. Potential regulatory pathways of T helper cells were identified through Mantel tests and Kyoto Encyclopedia of Genes and Genomes (KEGG) database enrichment analysis. RESULTS: Mendelian randomization analysis revealed an inhibitory effect of naïve CD4+ T cells on LUAD (false discovery rate < 0.05), which was corroborated by observational experiments using TCGA database. Specifically, T helper cell type 2 demonstrated a promotive effect on LUAD in terms of overall, disease-free, and progression-free survival (p < 0.05). Moreover, regulatory T cells exhibited a protective effect on LUAD in terms of disease-specific survival (p < 0.01). Concurrently, we explored the overall impact of naïve CD4+ T cell differentiation subtypes on LUAD, revealing upregulation in pathways such as neutrophil degranulation, MAPK family signaling pathways, and platelet activation, signaling, and aggregation. CONCLUSION: Naïve CD4+ T cells and their differentiated counterparts play essential regulatory roles in the tumor immune microenvironment, demonstrating bidirectionality in their effects.Thus, elucidating the mechanisms and developing novel cell differentiation-inducing agents will benefit anti-cancer therapy.

Genome-wide and cell-type-selective profiling of in vivo small noncoding RNA:target RNA interactions by chimeric RNA sequencing.

Small noncoding RNAs (sncRNAs) regulate biological processes by impacting post-transcriptional gene expression through repressing the translation and levels of targeted transcripts. Despite the clear biological importance of sncRNAs, approaches to unambiguously define genome-wide sncRNA:target RNA interactions remain challenging and not widely adopted. We present CIMERA-seq, a robust strategy incorporating covalent ligation of sncRNAs to their target RNAs within the RNA-induced silencing complex (RISC) and direct detection of in vivo interactions by sequencing of the resulting chimeric RNAs. Modifications are incorporated to increase the capacity for processing low-abundance samples and permit cell-type-selective profiling of sncRNA:target RNA interactions, as demonstrated in mouse brain cortex. CIMERA-seq represents a cohesive and optimized method for unambiguously characterizing the in vivo network of sncRNA:target RNA interactions in numerous biological contexts and even subcellular fractions. Genome-wide and cell-type-selective CIMERA-seq enhances researchers' ability to study gene regulation by sncRNAs in diverse model systems and tissue types.

Precise detection of cell-type-specific domains in spatial transcriptomics.

Cell-type-specific domains are the anatomical domains in spatially resolved transcriptome (SRT) tissues where particular cell types are enriched coincidentally. It is challenging to use existing computational methods to detect specific domains with low-proportion cell types, which are partly overlapped with or even inside other cell-type-specific domains. Here, we propose De-spot, which synthesizes segmentation and deconvolution as an ensemble to generate cell-type patterns, detect low-proportion cell-type-specific domains, and display these domains intuitively. Experimental evaluation showed that De-spot enabled us to discover the co-localizations between cancer-associated fibroblasts and immune-related cells that indicate potential tumor microenvironment (TME) domains in given slices, which were obscured by previous computational methods. We further elucidated the identified domains and found that Srgn may be a critical TME marker in SRT slices. By deciphering T cell-specific domains in breast cancer tissues, De-spot also revealed that the proportions of exhausted T cells were significantly increased in invasive vs. ductal carcinoma.

scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data.

Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook the biologically meaningful relationships between genes, opting to reduce all genes to a unified data space. We assume that such relationships can aid in characterizing cell type features and improving cell type recognition accuracy. To this end, we introduce scPriorGraph, a dual-channel graph neural network that integrates multi-level gene biosemantics. Experimental results demonstrate that scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance in cell type identification.

Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data.

Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level. However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 µm are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses to estimate the exact cellular proportions for each spatial spot, and to prospect the future directions of this field.

SpatialCTD: A Large-Scale Tumor Microenvironment Spatial Transcriptomic Dataset to Evaluate Cell Type Deconvolution for Immuno-Oncology.

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at a multicellular resolution that is more cost-effective. The task of cell type deconvolution has been introduced to disentangle discrete cell types from such multicellular spots. However, existing benchmark datasets for cell type deconvolution are either generated from simulation or limited in scale, predominantly encompassing data on mice and are not designed for human immuno-oncology. To overcome these limitations and promote comprehensive investigation of cell type deconvolution for human immuno-oncology, we introduce a large-scale spatial transcriptomic deconvolution benchmark dataset named SpatialCTD, encompassing 1.8 million cells and 12,900 pseudo spots from the human tumor microenvironment across the lung, kidney, and liver. In addition, SpatialCTD provides more realistic reference than those generated from single-cell RNA sequencing (scRNA-seq) data for most reference-based deconvolution methods. To utilize the location-aware SpatialCTD reference, we propose a graph neural network-based deconvolution method (i.e., GNNDeconvolver). Extensive experiments show that GNNDeconvolver often outperforms existing state-of-the-art methods by a substantial margin, without requiring scRNA-seq data. To enable comprehensive evaluations of spatial transcriptomics data from flexible protocols, we provide an online tool capable of converting spatial transcriptomic data from various platforms (e.g., 10× Visium, MERFISH, and sci-Space) into pseudo spots, featuring adjustable spot size. The SpatialCTD dataset and GNNDeconvolver implementation are available at https://github.com/OmicsML/SpatialCTD, and the online converter tool can be accessed at https://omicsml.github.io/SpatialCTD/.

Heterogeneity effects of bisphenol A and its substitute, fluorene-9-bisphenol, on intestinal homeostasis.

Bisphenol A (BPA) and its substitute fluorene-9-bisphenol (BHPF) are used in consumer products; however, their toxic effects on intestinal epithelium remain largely unknown. In this study, we combined intestinal organoids and single-cell RNA sequencing to investigate the impact of BPA and BHPF exposure on intestinal cell composition, differentiation, and function. Both compounds inhibited the growth of small intestinal organoids, with BHPF exhibiting a more potent inhibitory effect. BPA and BHPF did not significantly alter the overall cell type composition; however, they led to different alterations in cell-cell communications. Gene Ontology enrichment analysis showed that BPA and BHPF exposures affected various biological processes, such as glutathione transferase activity, antioxidant activity, and lipid metabolism, in cell type-specific and compound-dependent manners. Trajectory analysis demonstrated that BPA and BHPF altered the differentiation trajectory of the intestinal cells. To further connect the cellular mechanism to the phenotypic impact in vivo, we constructed a mouse model exposed to BPA or BHPF and observed significant alterations in intestinal morphology, including reduced crypt depth and villus length and impaired stem cell proliferation and self-renewal. These results provide novel insights into the cell type-specific effects of BPA and BHPF on the intestinal epithelium and highlight the potential risks of exposure to these compounds. Our findings underscore the importance of evaluating the safety of BPA substitutes and contribute to a better understanding of the effects of environmental chemicals on gut health.

Mining alternative splicing patterns in scRNA-seq data using scASfind.

Single-cell RNA-seq (scRNA-seq) is widely used for transcriptome profiling, but most analyses focus on gene-level events, with less attention devoted to alternative splicing. Here, we present scASfind, a novel computational method to allow for quantitative analysis of cell type-specific splicing events using full-length scRNA-seq data. ScASfind utilizes an efficient data structure to store the percent spliced-in value for each splicing event. This makes it possible to exhaustively search for patterns among all differential splicing events, allowing us to identify marker events, mutually exclusive events, and events involving large blocks of exons that are specific to one or more cell types.

Circuit dynamics of superficial and deep CA1 pyramidal cells and inhibitory cells in freely moving macaques.

Diverse neuron classes in hippocampal CA1 have been identified through the heterogeneity of their cellular/molecular composition. How these classes relate to hippocampal function and the network dynamics that support cognition in primates remains unclear. Here, we report inhibitory functional cell groups in CA1 of freely moving macaques whose diverse response profiles to network states and each other suggest distinct and specific roles in the functional microcircuit of CA1. In addition, pyramidal cells that were grouped by their superficial or deep layer position differed in firing rate, burstiness, and sharp-wave ripple-associated firing. They also showed strata-specific spike-timing interactions with inhibitory cell groups, suggestive of segregated neural populations. Furthermore, ensemble recordings revealed that cell assemblies were preferentially organized according to these strata. These results suggest that hippocampal CA1 in freely moving macaques bears a sublayer-specific circuit organization that may shape its role in cognition.

Polystyrene nanoplastics induce cell type-dependent secondary wall reinforcement in rice (Oryza sativa) roots and reduce root hydraulic conductivity.

Nanoplastics (NPs) have been demonstrated the ability to penetrate plant roots and cause stress. However, the extent of NPs penetration into various root tissues and the corresponding plant defense mechanisms remain unclear. This study examined the penetration and accumulation patterns of polystyrene nanoplastics (PS-NPs) in different cell types within rice roots, and explored how the roots quickly modify their cell wall structure in response. The findings showed that fully developed sclerenchyma cells in rice roots effectively prevented the invasion of PS-NPs. Meanwhile, PS-NPs triggered the accumulation of lignin and suberin in specific cells such as the exodermis, sclerenchyma, and xylem vessels. PS-NPs at a concentration of 50 mg L-1 increased cell wall thickness by 18.6 %, 21.1 %, and 22.4 % in epidermis, exodermis, and sclerenchyma cells, respectively, and decreased root hydraulic conductivity by 14.8 %. qPCR analysis revealed that PS-NPs influenced the cell wall synthesis pathway, promoting the deposition of lignin and suberin monomers on the secondary wall through the up-regulation of genes such as OsLAC and OsABCG. These results demonstrate that PS-NPs can induce cell type-specific strengthening of secondary walls and barrier formation in rice roots, suggesting the potential role of plant secondary wall development in mitigating NPs contamination risks in crops.

A Novel Role for the Histone Demethylase JMJD3 in Mediating Heroin-Induced Relapse-Like Behaviors.

BACKGROUND: Epigenetic changes that lead to long-term neuroadaptations following opioid exposure are not well understood. We examined how histone demethylase JMJD3 in the nucleus accumbens (NAc) influences heroin seeking after abstinence from self-administration. METHODS: Male Sprague Dawley rats were trained to self-administer heroin. Western blotting and quantitative polymerase chain reaction were performed to quantify JMJD3 and bone morphogenetic protein (BMP) pathway expression in the NAc (n = 7-11/group). Pharmacological inhibitors or viral expression vectors were microinfused into the NAc to manipulate JMJD3 or the BMP pathway member SMAD1 (n = 9-11/group). The RiboTag capture method (n = 3-5/group) and viral vectors (n = 7-8/group) were used in male transgenic rats to identify the contributions of D1- and D2-expressing medium spiny neurons in the NAc. Drug seeking was tested by cue-induced response previously paired with drug infusion. RESULTS: Levels of JMJD3 and phosphorylated SMAD1/5 in the NAc were increased after 14 days of abstinence from heroin self-administration. Pharmacological and virus-mediated inhibition of JMJD3 or the BMP pathway attenuated cue-induced seeking. Pharmacological inhibition of BMP signaling reduced JMJD3 expression and H3K27me3 levels. JMJD3 bidirectionally affected seeking: expression of the wild-type increased cue-induced seeking whereas expression of a catalytic dead mutant decreased it. JMJD3 expression was increased in D2+ but not D1+ medium spiny neurons. Expression of the mutant JMJD3 in D2+ neurons was sufficient to decrease cue-induced heroin seeking. CONCLUSIONS: JMJD3 mediates persistent cellular and behavioral adaptations that underlie heroin relapse, and this activity is regulated by the BMP pathway.

On the evolutionary developmental biology of the cell.

Organisms are complex assemblages of cells, cells that produce light, shoot harpoons, and secrete glue. Therefore, identifying the mechanisms that generate novelty at the level of the individual cell is essential for understanding how multicellular life evolves. For decades, the field of evolutionary developmental biology (Evo-Devo) has been developing a framework for connecting genetic variation that arises during embryonic development to the emergence of diverse adult forms. With increasing access to new single cell 'omics technologies and an array of techniques for manipulating gene expression, we can now extend these inquiries inward to the level of the individual cell. In this opinion, I argue that applying an Evo-Devo framework to single cells makes it possible to explore the natural history of cells, where this was once only possible at the organismal level.

Target-Oriented Reference Construction for supervised cell-type identification in scRNA-seq.

Cell-type identification is the most crucial step in single cell RNA-seq (scRNA-seq) data analysis, for which the supervised cell-type identification method is a desired solution due to the accuracy and efficiency. The performance of such methods is highly dependent on the quality of the reference data. Even though there are many supervised cell-type identification tools, there is no method for selecting and constructing reference data. Here we develop Target-Oriented Reference Construction (TORC), a widely applicable strategy for constructing reference given target dataset in scRNA-seq supervised cell-type identification. TORC alleviates the differences in data distribution and cell-type composition between reference and target. Extensive benchmarks on simulated and real data analyses demonstrate consistent improvements in cell-type identification from TORC. TORC is freely available at https://github.com/weix21/TORC.