Statistical significance of cluster membership for unsupervised evaluation of cell identities

Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon

The National Cancer Institute (NCI) supports numerous research consortia that rely on imaging technologies to study cancerous tissues. To foster collaboration and innovation in this field, the Image Analysis Working Group (IAWG) was created in 2019. As multiplexed imaging techniques grow in scale and complexity, more advanced computational methods are required beyond traditional approaches like segmentation and pixel intensity quantification. In 2022, the IAWG held a virtual hackathon focused on addressing challenges in analyzing complex, high-dimensional datasets from fixed cancer tissues. The hackathon addressed key challenges in three areas: (1) cell type classification and assessment, (2) spatial data visualization and translation, and (3) scaling image analysis for large, multi-terabyte datasets. Participants explored the limitations of current automated analysis tools, developed potential solutions, and made significant progress during the hackathon. Here we provide a summary of the efforts and resultant resources and highlight remaining challenges facing the research community as emerging technologies are integrated into diverse imaging modalities and data analysis platforms.

Epithelial and immune transcriptomic characteristics and possible regulatory mechanisms in asthma exacerbation: insights from integrated studies

Background

Asthma exacerbation significantly contribute to disease mortality and result in heightened health care expenditures. This study was aimed at gaining important new insights into the heterogeneity of epithelial and immune cells and elucidating key regulatory genes involved in the pathogenesis of asthma exacerbation.

Methods

Functional enrichment, pseudotime, metabolism and cell-cell communication analyses of epithelial cells and immune cells in single-cell RNA sequencing (scRNA-seq) dataset were applied. Immune infiltration analysis was performed in bulk RNA sequencing (bulk RNA-seq) dataset. Key regulatory genes were obtained by taking the intersection of the differentially expressed genes (DEGs) between control and asthma group in epithelial cells, immune cells and bulk RNA-seq data. Asthma animal and in vitro cell line models were established to verify the key regulatory genes expression by employing quantitative reverse transcription polymerase chain reaction (qRT-PCR).

Results

ScRNA-seq analysis identified 7 epithelial subpopulations and 14 distinct immune cell types based on gene expression profiles. Further analysis demonstrated that these cells manifested high heterogeneity at the levels of functional variations, dynamics, communication patterns and metabolic changes. Notably, TMPRSS11A, TUBA1A, SCEL, ICAM4, TMPRSS11B, IGFBP2, CLC, NFAM1 and F13A1 were identified as key regulatory genes of asthma. The results of the qRT-PCR demonstrated that the 9 key regulatory genes were involved in asthma.

Conclusions

We systematically explored epithelial and immune characteristics in asthma exacerbation and identified 9 key regulatory genes underlying asthma occurrence and progression, which may be valuable for providing new insights into the cellular and molecular mechanisms driving asthma exacerbations.

SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.

Inference after latent variable estimation for single-cell RNA sequencing data

In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, representing some aspect of the cell's state. They then test each gene for association with the estimated latent variable. If the same data are used for both of these steps, then standard methods for computing p-values in the second step will fail to achieve statistical guarantees such as Type 1 error control. Furthermore, approaches such as sample splitting that can be applied to solve similar problems in other settings are not applicable in this context. In this article, we introduce count splitting, a flexible framework that allows us to carry out valid inference in this setting, for virtually any latent variable estimation technique and inference approach, under a Poisson assumption. We demonstrate the Type 1 error control and power of count splitting in a simulation study and apply count splitting to a data set of pluripotent stem cells differentiating to cardiomyocytes.

Multi-omics characteristics of tumor-associated macrophages in the tumor microenvironment of gastric cancer and their exploration of immunotherapy potential

The incidence and mortality rate of gastric cancer (GC) have remained high worldwide. Although some progress has been made in immunotargeted therapy, the treatment effect remains limited. With more attention has been paid to the immune potential of tumor-associated macrophages (TAMs), but the specific mechanisms of tumor immunity are still unclear. Thus, we screened marker genes in TAMs differentiation (MDMs) through single-cell RNA sequencing, and combined with GC transcriptome data from TCGA and GEO databases, the clinical and TME characteristics, prognostic differences, immune infiltration, and drug sensitivity among different subtypes of patients with GC in different data sets were analyzed. A prognostic model of GC was constructed to evaluate the prognosis and immunotherapy response of patients with GC. In this study, we extensively studied the mutations in MDMs such as CGN, S100A6, and C1QA, and found differences in the infiltration of immune cells and immune checkpoints including M2 TAMs, T cells, CD274, and CTLA4 in different GC subtypes. In the model, we constructed a predictive scoring system with high accuracy and screened out key MDMs-related genes associated with prognosis and M2 TAMs, among which VKORC1 may be involved in GC progression and iron death in tumor cells. Therefore, this study explores the therapeutic strategy of TAMs reprogramming in-depth, providing new ideas for the clinical diagnosis, treatment, and prognosis assessment of GC.

Integrated analysis reveals Atf3 promotes neuropathic pain via orchestrating JunB mediated release of inflammatory cytokines in DRG macrophage

The dorsal root ganglion (DRG) is actively involved in the development of neuropathic pain (NP), serving as an intermediate station for pain signals from the peripheral nervous system to the central nervous system. The mechanism by which DRG is involved in NP regulation is not fully understood. The immune system plays a pivotal role in the physiological and pathological states of the human body. In recent years, the immune system has been thought to play an increasingly important role in the pathogenesis of NP. The immune system plays a key role in pain through specific immune cells and their immune-related genes (IRGs). However, the mechanism by which IRGs of DRG regulate NP action has not been fully elucidated. Here, we performed Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of IRGs in DRG bulk-RNA sequencing data from spared nerve injury (SNI) model mice and found that their IRGs were enriched in many pathways, especially in the immune response pathway. Subsequently, we analyzed single-cell RNA sequencing (scRNA-seq) data from DRGs extracted from the SNI model and identified eight cell populations. Among them, the highest IRG activity was presented in macrophages. Next, we analyzed the scRNA and bulk-sequencing data and deduced five common transcription factors (TFs) from differentially expressed genes (DEGs). The protein-protein interaction (PPI) network suggested that Atf3 and JunB are closely related. In vitro experiments, we verified that the protein and mRNA expressions of Atf3 and JunB were up-regulated in macrophages after lipopolysaccharide (LPS) stimulation. Moreover, the down-regulation of Atf3 reduced the release of inflammatory cytokines and decreased the protein and mRNA expression levels of JunB. The down-regulation of JunB also reduced the release of inflammatory cytokines. Furthermore, overexpression of JunB attenuated the effect of Atf3 down-regulation in reducing the release of inflammatory cytokines. Therefore, we speculated that Atf3 might promote NP through JunB-mediated release of inflammatory factors in DRG macrophages.

Atonal homolog 7 (ATOH7) confers neuroprotection for photoreceptor cells in glaucoma via inhibition of the notch pathway

Single-cell RNA sequencing (scRNA-seq) technologies enable the profiling and analysis of the transcriptomes of single cells and hold promise for clarifying gene mechanisms at single-cell resolution. We based this study on scRNA-seq data to reveal glaucoma-related genes and downstream pathways with neuroprotection effects. The scRNA-seq datasets related to glaucoma of retinal tissue samples of human beings and Atonal Homolog 7 (ATOH7)-null mice were obtained from the GEO database. The 74 top marker genes and 20 cell clusters were obtained in human retinal tissue samples. The key gene ATOH7 was found after the intersection with genes from GeneCards data. In the ATOH7-null mouse retinal tissue samples, pseudotime inference demonstrated significant changes in cell differentiation. Moreover, mouse retinal photoreceptor cells (PRCs) were cultured and treated with lentivirus carrying oe-ATOH7 alone or in combination with Notch signaling pathway activator Jagged-1/FC, after which cell biological functions were determined. The involvement of ATOH7 in glaucoma was identified through regulating PRCs. Furthermore, ATOH7 conferred neuroprotection in PRCs in glaucoma by mediating the Notch signaling pathway. In vitro data confirmed that ATOH7 overexpression promoted the differentiation of PRCs and inhibited their apoptosis by suppressing the Notch signaling pathway. The evidence provided by our study highlighted the involvement of ATOH7 in the blockade of the Notch signaling pathway, resulting in the neuroprotection for PRCs in glaucoma.

Addressing persistent challenges in digital image analysis of cancerous tissues

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.

Selective inference for k-means clustering

We consider the problem of testing for a difference in means between clusters of observations identified via k -means clustering. In this setting, classical hypothesis tests lead to an inflated Type I error rate. In recent work, Gao et al. (2022) considered a related problem in the context of hierarchical clustering. Unfortunately, their solution is highly-tailored to the context of hierarchical clustering, and thus cannot be applied in the setting of k -means clustering. In this paper, we propose a p-value that conditions on all of the intermediate clustering assignments in the k -means algorithm. We show that the p-value controls the selective Type I error for a test of the difference in means between a pair of clusters obtained using k -means clustering in finite samples, and can be efficiently computed. We apply our proposal on hand-written digits data and on single-cell RNA-sequencing data.

FDX1 regulates cellular protein lipoylation through direct binding to LIAS

Ferredoxins are a family of iron-sulfur (Fe-S) cluster proteins that serve as essential electron donors in numerous cellular processes that are conserved through evolution. The promiscuous nature of ferredoxins as electron donors enables them to participate in many metabolic processes including steroid, heme, vitamin D and Fe-S cluster biosynthesis in different organisms. However, the unique natural function(s) of each of the two human ferredoxins (FDX1 and FDX2) are still poorly characterized. We recently reported that FDX1 is both a crucial regulator of copper ionophore induced cell death and serves as an upstream regulator of cellular protein lipoylation, a mitochondrial lipid-based post translational modification naturally occurring on four mitochondrial enzymes that are crucial for TCA cycle function. Here we show that FDX1 regulates protein lipoylation by directly binding to the lipoyl synthase (LIAS) enzyme and not through indirect regulation of cellular Fe-S cluster biosynthesis. Metabolite profiling revealed that the predominant cellular metabolic outcome of FDX1 loss-of-function is manifested through the regulation of the four lipoylation-dependent enzymes ultimately resulting in loss of cellular respiration and sensitivity to mild glucose starvation. Transcriptional profiling of cells growing in either normal or low glucose conditions established that FDX1 loss-of-function results in the induction of both compensatory metabolism related genes and the integrated stress response, consistent with our findings that FDX1 loss-of-functions is conditionally lethal. Together, our findings establish that FDX1 directly engages with LIAS, promoting cellular protein lipoylation, a process essential in maintaining cell viability under low glucose conditions.

Graphia: A platform for the graph-based visualisation and analysis of high dimensional data

Graphia is an open-source platform created for the graph-based analysis of the huge amounts of quantitative and qualitative data currently being generated from the study of genomes, genes, proteins metabolites and cells. Core to Graphia’s functionality is support for the calculation of correlation matrices from any tabular matrix of continuous or discrete values, whereupon the software is designed to rapidly visualise the often very large graphs that result in 2D or 3D space. Following graph construction, an extensive range of measurement algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are available, for graph exploration and analysis. Combined, these provide a powerful solution for the interpretation of high-dimensional data from many sources, or data already in the form of a network or equivalent adjacency matrix. Several use cases of Graphia are described, to showcase its wide range of applications in the analysis biological data. Graphia runs on all major desktop operating systems, is extensible through the deployment of plugins and is freely available to download from https://graphia.app/.

Graphia is a new visual analytics platform specifically created for the network-based analysis of large and complex data, such as that generated in huge amounts by modern biological analyses. It works in a data agnostic, hypothesis-free manner to generate correlation networks from any table of numerical or discrete values, thereafter providing a means to rapidly visualise the often very large networks that result, in either 2D or 3D space. Following network construction, the tool offers an extensive range of analysis algorithms, routines for network transformation, and options for the visualisation of metadata. This provides a powerful analysis solution for the exploration and interpretation of high-dimensional data from any source, as well as any data already defined as a network. Several use cases of Graphia are described to showcase its wide range of applications in the analysis biological data. Graphia is open source and free to all.

Analyzing Spatial Transcriptomics Data Using Giotto

Spatial transcriptomic technologies have been developed rapidly in recent years. The addition of spatial context to expression data holds the potential to revolutionize many fields in biology. However, the lack of computational tools remains a bottleneck that is preventing the broader utilization of these technologies. Recently, we have developed Giotto as a comprehensive, generally applicable, and user-friendly toolbox for spatial transcriptomic data analysis and visualization. Giotto implements a rich set of algorithms to enable robust spatial data analysis. To help users get familiar with the Giotto environment and apply it effectively in analyzing new datasets, we will describe the detailed protocols for applying Giotto without any advanced programming skills. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Getting Giotto set up for use Basic Protocol 2: Pre-processing Basic Protocol 3: Clustering and cell-type identification Basic Protocol 4: Cell-type enrichment and deconvolution analyses Basic Protocol 5: Spatial structure analysis tools Basic Protocol 6: Spatial domain detection by using a hidden Markov random field model Support Protocol 1: Spatial proximity-associated cell-cell interactions Support Protocol 2: Assembly of a registered 3D Giotto object from 2D slices.

Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods

Single-cell transcriptomics can profile thousands of cells in a single experiment and identify novel cell types, states and dynamics in a wide variety of tissues and organisms. Standard experimental protocols and analysis workflows have been developed to create single-cell transcriptomic maps from tissues. This tutorial focuses on how to interpret these data to identify cell types, states and other biologically relevant patterns with the objective of creating an annotated map of cells. We recommend a three-step workflow including automatic cell annotation (wherever possible), manual cell annotation and verification. Frequently encountered challenges are discussed, as well as strategies to address them. Guiding principles and specific recommendations for software tools and resources that can be used for each step are covered, and an R notebook is included to help run the recommended workflow. Basic familiarity with computer software is assumed, and basic knowledge of programming (e.g., in the R language) is recommended.

Transcription cofactor GRIP1 differentially affects myeloid cell-driven neuroinflammation and response to IFN-β therapy

Macrophages (MФ) and microglia (MG) are critical in the pathogenesis of multiple sclerosis (MS) and its mouse model, experimental autoimmune encephalomyelitis (EAE). Glucocorticoids (GCs) and interferon β (IFN-β) are frontline treatments for MS, and disrupting each pathway in mice aggravates EAE. Glucocorticoid receptor-interacting protein 1 (GRIP1) facilitates both GR and type I IFN transcriptional actions; hence, we evaluated the role of GRIP1 in neuroinflammation. Surprisingly, myeloid cell-specific loss of GRIP1 dramatically reduced EAE severity, immune cell infiltration of the CNS, and MG activation and demyelination specifically during the neuroinflammatory phase of the disease, yet also blunted therapeutic properties of IFN-β. MФ/MG transcriptome analyses at the bulk and single-cell levels revealed that GRIP1 deletion attenuated nuclear receptor, inflammatory and, interestingly, type I IFN pathways and promoted the persistence of a homeostatic MG signature. Together, these results uncover the multifaceted function of type I IFN in MS/EAE pathogenesis and therapy, and an unexpectedly permissive role of myeloid cell GRIP1 in neuroinflammation.

DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.

Identifying temporal molecular signatures underlying cardiovascular diseases: A data science platform

OBJECTIVE

During cardiovascular disease progression, molecular systems of myocardium (e.g., a proteome) undergo diverse and distinct changes. Dynamic, temporally-regulated alterations of individual molecules underlie the collective response of the heart to pathological drivers and the ultimate development of pathogenesis. Advances in high-throughput omics technologies have enabled cost-effective, temporal profiling of targeted systems in animal models of human diseases. However, computational analysis of temporal patterns from omics data remains challenging. In particular, bioinformatic pipelines involving unsupervised statistical approaches to support cardiovascular investigations are lacking, which hinders one's ability to extract biomedical insights from these complex datasets.

APPROACH AND RESULTS

We developed a non-parametric data analysis platform to resolve computational challenges unique to temporal omics datasets. Our platform consists of three modules. Module I preprocesses the temporal data using either cubic splines or principal component analysis (PCA), and it simultaneously accomplishes the tasks on missing data imputation and denoising. Module II performs an unsupervised classification by K-means or hierarchical clustering. Module III evaluates and identifies biological entities (e.g., molecular events) that exhibit strong associations to specific temporal patterns. The jackstraw method for cluster membership has been applied to estimate p-values and posterior inclusion probabilities (PIPs), both of which guided feature selection. To demonstrate the utility of the analysis platform, we employed a temporal proteomics dataset that captured the proteome-wide dynamics of oxidative stress induced post-translational modifications (O-PTMs) in mouse hearts undergoing isoproterenol (ISO)-induced hypertrophy.

CONCLUSION

We have created a platform, CV.Signature.TCP, to identify distinct temporal clusters in omics datasets. We presented a cardiovascular use case to demonstrate its utility in unveiling biological insights underlying O-PTM regulations in cardiac remodeling. This platform is implemented in an open source R package (https://github.com/UCLA-BD2K/CV.Signature.TCP).