Tabula Muris Research Articles

The opposite aging effect to single cell transcriptome profile among cell subsets.

Comparing transcriptome profiling between younger and older samples reveals genes related to aging and provides insight into the biological functions affected by aging. Recent research has identified sex, tissue, and cell type-specific age-related changes in gene expression. This study reports the overall picture of the opposite aging effect, in which aging increases gene expression in one cell subset and decreases it in another cell subset. Using the Tabula Muris Senis dataset, a large public single-cell RNA sequencing dataset from mice, we compared the effects of aging in different cell subsets. As a result, the opposite aging effect was observed widely in the genes, particularly enriched in genes related to ribosomal function and translation. The opposite aging effect was observed in the known aging-related genes. Furthermore, the opposite aging effect was observed in the transcriptome diversity quantified by the number of expressed genes and the Shannon entropy. This study highlights the importance of considering the cell subset when intervening with aging-related genes.

Predicting Cell Type-Specific Extracellular Vesicle Biology Using an Organism-Wide Single Cell Transcriptomic Atlas - Insights from the Tabula Muris

Background: Extracellular vesicles (EVs) like exosomes are functional nanoparticles traffcked between cells and found in every biofluid. An incomplete understanding of which cells, from which tissues, are traffcking EVs in vivo has limited our ability to use EVs as biomarkers and therapeutics. However, recent discoveries have linked EV secretion to expression of genes and proteins responsible for EV biogenesis and found as cargo, which suggests that emerging “cell atlas” datasets could be used to begin understanding EV biology at the level of the organism and possibly in rare cell populations. To explore this possibility, here we analyzed 67 genes that are directly implicated in EV biogenesis and secretion, or carried as cargo, in ~44,000 cells obtained from 117 cell populations of the Tabula Muris. Methods: We obtained normalized count (FPKM) data from 44,779 FACS-sorted cells described in the Tabula Muris. These data were freely obtained from the Tabula Muris web portal operated by the Chan-Zuckerberg Biohub. We selected 67 EV genes based on their direct role in the regulation of endosome biogenesis, protein sorting, secretion or cargo. These genes include the endosome sorting complexes required for transport (ESCRT), multiple Rab GTPases, tetraspanins and syndecans. To identify the most abundant EV genes, we plotted mean expression (FPKM) across all cells against the fraction of cells with gene expression > 50 FPKM. To measure gene expression variance, we calculated %CV for each gene within each cell population and across all cell populations. For gene co-expression analysis, we performed linear regressions comparing selected EV genes (Cd9, Cd63, Cd81, Sdc4 and Sdcbp) to all other genes, within each cell population. Results: We found that the most abundant EV cargo proteins (tetraspanins (Cd9, Cd63 and Cd81) and syndecans (Sdc4 and Sdcbp)) are also the most abundant EV genes expressed across all cell populations. However, the expression of these genes varied greatly among cell populations. For example, Cd81 is most abundant in brain glial populations and multiple endothelial cell populations. By contrast, Cd9 expression is highest in "barrier" cells that support innate immunity while Cd63, a classic marker of exosomes, is most abundantly expressed by stem cells. Expression variance analysis was used to compare the transcriptional behavior of these abundant EV genes (Cd9, Cd63, Cd81, Sdc4 and Sdcbp). We found that Cd63 is the most dynamic EV gene while others are more constitutively expressed. This finding may help explain why Cd63 is typically found either within the cytoplasm or outside of the cell on EVs, whereas Cd9 and Cd81 are abundant both on the cell surface and on EVs. Finally, we used EV gene co-expression analysis to define cell population-specific transcriptional networks. We discovered that EV gene co-expression networks are heavily dependent on the EV cargo gene examined with different cell populations driving clustering. Conclusions: Our analysis has provided at least four independent observations which advance our understanding of EV biology. These are that: 1) abundant EV cargo genes are differentially expressed among cell populations, 2) skeletal muscle stem cells display the highest expression of both Cd63 and Sdc4, 3) Cd81 may be a primary EV cargo traffcked in the brain whiand 4) that rare cell populations can secrete EVs and thereby influence tissue biology. This analysis is the first, to our knowledge, to predict tissue- and cell type-specific EV biology at the level of the organism. As such, we expect this resource to be the first of many valuable tools for predicting the endogenous impact of specific cell populations on EV function in health and disease. National Institutes on Aging (R01AG078859). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Identification of ANKRD13D as a potential target in renal cell carcinomas.

The correlation of the expression of ankyrin repeat domain (ANKRD) family members with renal cell carcinoma prognosis was investigated. The GEPIA2, GEO2R, UALCAN, GDC, OncoLnc, TIMER, PanglaoDB, CancerSEA, and Tabula Muris databases were used. Twelve ANKRD family members were identified as having overexpressed renal cell carcinoma samples. The ANKRD13D was identified as a renal cell carcinoma-specific target by cross-referencing the multiple survival databases. To clarify the role of ANKRD13D, the expression of NAKRD13D was analyzed at the single-cell level. ANKRD13D was mainly expressed in immune cells and positively correlated with Treg cell infiltration. The expression of ANKRD13D was also positively correlated with PDCD1, CTLA4, LAG3, TNFSF14, and ISG20. The overexpression of ANKRD13D in Treg was confirmed using reverse transcription-quantitative polymerase chain reaction. The structure of ANKRD13D was predicted using AlphaFold. In conclusion, we identified ANKRD13D as a key immune regulator, and targeting ANKRD13D with immune checkpoints blockade may be a promoting strategy for renal cell carcinoma immunotherapy.

Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis

BackgroundFeature selection is an essential task in single-cell RNA-seq (scRNA-seq) data analysis and can be critical for gene dimension reduction and downstream analyses, such as gene marker identification and cell type classification. Most popular methods for feature selection from scRNA-seq data are based on the concept of differential distribution wherein a statistical model is used to detect changes in gene expression among cell types. Recent development of deep learning-based feature selection methods provides an alternative approach compared to traditional differential distribution-based methods in that the importance of a gene is determined by neural networks.ResultsIn this work, we explore the utility of various deep learning-based feature selection methods for scRNA-seq data analysis. We sample from Tabula Muris and Tabula Sapiens atlases to create scRNA-seq datasets with a range of data properties and evaluate the performance of traditional and deep learning-based feature selection methods for cell type classification, feature selection reproducibility and diversity, and computational time.ConclusionsOur study provides a reference for future development and application of deep learning-based feature selection methods for single-cell omics data analyses.

Unveiling the immune system aging in single-cell resolution.

This commentary investigates the findings presented in the article by Yang et al. in 2023, published in the Journal of Leukocyte Biology. This commentary first summarizes the spatial-temporal dynamics of regulatory T cells derived from mice (Tabula Muris Senis) of different ages (3, 18, and 24 mo) at different anatomic niches like lymph nodes and bone marrow. We also reported possible combinations of receptor-ligand interactions among T follicular regulatory cells, T follicular helper cells, and germinal center B cells, such as the calmodulin/Fas axis and PSGL-1/L-selectin axis. Then, we have elaborated on the significance of understanding aging regulatory T cells and offered some possible future research directions for Yang et al., contributing to a critical analysis of their recent study. Building on these foundations, further investigations and studies can be conducted to delve deeper into the mechanisms by which regulatory T cells influence health upon aging, potentially unveiling novel therapeutic targets to ameliorate age-related pathogenicity.

ScDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication

Dysregulation of intercellular communication is a hallmark of aging. To better quantify and explore changes in intercellular communication, we present scDiffCom and scAgeCom. scDiffCom is an R package, relying on approximately 5,000 curated ligand–receptor interactions, that performs differential intercellular communication analysis between two conditions from single-cell transcriptomics data. Built upon scDiffCom, scAgeCom is an atlas of age-related cell–cell communication changes covering 23 mouse tissues from 58 single-cell RNA sequencing datasets from Tabula Muris Senis and the Calico murine aging cell atlas. It offers a comprehensive resource of tissue-specific and sex-specific aging dysregulations and highlights age-related intercellular communication changes widespread across the whole body, such as the upregulation of immune system processes and inflammation, the downregulation of developmental processes, angiogenesis and extracellular matrix organization and the deregulation of lipid metabolism. Our analysis emphasizes the relevance of the specific ligands, receptors and cell types regulating these processes. The atlas is available online (https://scagecom.org).

Aging impairs the ability of vascular endothelial stem cells to generate endothelial cells in mice

Tissue-resident vascular endothelial stem cells (VESCs), marked by expression of CD157, possess long-term repopulating potential and contribute to vascular regeneration and homeostasis in mice. Stem cell exhaustion is regarded as one of the hallmarks of aging and is being extensively studied in several types of tissue-resident stem cells; however, how aging affects VESCs has not been clarified yet. In the present study, we isolated VESCs from young and aged mice to compare their potential to differentiate into endothelial cells in vitro and in vivo. Here, we report that the number of liver endothelial cells (ECs) including VESCs was lower in aged (27–28 month-old) than young (2–3 month-old) mice. In vitro culture of primary VESCs revealed that the potential to generate ECs is impaired in aged VESCs isolated from liver and lung relative to young VESCs. Orthotopic transplantation of VESCs showed that aged VESCs and their progeny expand less efficiently than their young counterparts when transplanted into aged mice, but they are equally functional in young recipients. Gene expression analysis indicated that inflammatory signaling was more activated in aged ECs including VESCs. Using single-cell RNA sequencing data from the Tabula Muris Consortium, we show that T cells and monocyte/macrophage lineage cells including Kupffer cells are enriched in the aged liver. These immune cells produce IL-1β and several chemokines, suggesting the possible involvement of age-associated inflammation in the functional decline of VESCs with age.

A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures.

Accurately measuring biological age is crucial for improving healthcare for the elderly population. However, the complexity of aging biology poses challenges in how to robustly estimate aging and in how to interpret the biological significance of the traits used for estimation. Here we present SCALE, a statistical pipeline that quantifies biological aging in different tissues using explainable features learned from literature and single-cell transcriptomic data. Applying SCALE to the "Mouse Aging Cell Atlas" (Tabula Muris Senis) data, we identified tissue-level transcriptomic aging programs for over 20 murine tissues and created a multi-tissue resource of mouse quantitative aging-associated genes. We observe that SCALE correlates well with other age indicators, such as the accumulation of somatic mutations, and can distinguish subtle differences in aging even in cells of the same chronological age. We further compared SCALE with other transcriptomic and methylation 'clocks' in data of aging muscle stem cells, Alzheimer's disease, and heterochronic parabiosis. Our results confirm that SCALE is more generalizable and reliable in assessing biological aging in aging-related diseases and rejuvenating interventions. Overall, SCALE represents a valuable advancement in our ability to measure aging accurately, robustly, and interpretably in single cells.

Accurate and interpretable gene expression imputation on scRNA-seq data using IGSimpute.

Single-cell ribonucleic acid sequencing (scRNA-seq) enables the quantification of gene expression at the transcriptomic level with single-cell resolution, enhancing our understanding of cellular heterogeneity. However, the excessive missing values present in scRNA-seq data hinder downstream analysis. While numerous imputation methods have been proposed to recover scRNA-seq data, high imputation performance often comes with low or no interpretability. Here, we present IGSimpute, an accurate and interpretable imputation method for recovering missing values in scRNA-seq data with an interpretable instance-wise gene selection layer (GSL). IGSimpute outperforms 12 other state-of-the-art imputation methods on 13 out of 17 datasets from different scRNA-seq technologies with the lowest mean squared error as the chosen benchmark metric. We demonstrate that IGSimpute can give unbiased estimates of the missing values compared to other methods, regardless of whether the average gene expression values are small or large. Clustering results of imputed profiles show that IGSimpute offers statistically significant improvement over other imputation methods. By taking the heart-and-aorta and the limb muscle tissues as examples, we show that IGSimpute can also denoise gene expression profiles by removing outlier entries with unexpectedly high expression values via the instance-wise GSL. We also show that genes selected by the instance-wise GSL could indicate the age of B cells from bladder fat tissue of the Tabula Muris Senis atlas. IGSimpute can impute one million cells using 64min, and thus applicable to large datasets.

Abstract 6614: Spatial transcriptomic driven mechanistic model to investigate and predict pathogenesis of oligodendroglioma

Abstract Rationale: Oligodendrogliomas represent the second most common glioma in adults after glioblastoma. Recent studies conjecture that oligodendrocyte progenitor cells (OPCs) are precursors for glioma. The etiology of OPC derived oligodendrogliomas, the corresponding genetic program and genetic aberrations, and the underlying mechanisms leading to the pathogenesis of oligodendroglioma is yet to be explored. We hypothesize that genetic mechanisms mediating oligodendrogenesis (the process of OPCs maturing to oligodendrocytes (OL)) are temporally and spatially coordinated. In this study, we spearhead a comprehensive computational framework that utilizes a) Machine learning (ML) to interpret spatial transcriptomic (ST) images of a normal whole mouse brain dataset obtained from Vizgen’s MERFISH Mouse Brain Receptor Map and b) mathematical modeling to perturb the cellular mechanisms in the normal brain to understand the abnormalities leading to the pathogenesis of oligodendroglioma. Methods: Using ML for clustering and trajectory inference, we create a comprehensive spatial catalog of cell types. The cell types are validated using canonical genes from the scRNAseq mouse brain data from CZI Biohub’s Tabula Muris Consortium. From the cell types, we extract potential OL pseudotime differentiation (from immature to mature OLs) trajectories. These trajectories are used to develop a phenotype structured partial differentiation equation (PDE) model of oligodendrogenesis. This can be used to simulate both spatial and phenotypic interactions, differentiation status, and the pathogenesis of oligodendroglioma by mathematically perturbing the process of cellular proliferation and differentiation using genes known to cause oligodendroglioma: PDGFRA, OLIG1, MAS1. Results: We mathematically perturb cell differentiation lineages in normal oligodendrogenesis to simulate, and predict the effects of perturbation and how this leads to abnormal (cancer aiding) cell states. Our PDE simulations reinforce that OPC proliferation and OL differentiation occur more in the white matter in the normal mouse brain, and further postulate that OPC and OL differentiation occur more in gray matter in malignant mouse brains. Conclusions: Our study is one of the earliest attempts to integrate qualitative and scalable ML techniques with quantitative and interpretable mathematical modeling at the cellular, omics, and organ level using ST imaging and sequencing data. We develop and calibrate PDE mathematical models for robustly simulating, predicting, and studying the effects of perturbation from sparse, noisy single cell spatial measurements to forecast abnormal behavior in OLs based on spatial and (pseudo)temporal changes. In future work, we will apply our framework to jointly perturb differentiation trajectories of multiple cell types to understand their cumulative effects in tumor progression. Citation Format: Sandhya Prabhakaran, Heyrim Cho, Maximilian Strobl, Russell Rockne, Alexander R. Anderson. Spatial transcriptomic driven mechanistic model to investigate and predict pathogenesis of oligodendroglioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6614.

Whole organism profiling of the Timp gene family

Tissue inhibitor of metalloproteinases (TIMPs/Timps) are an endogenous family of widely expressed matrisome-associated proteins that were initially identified as inhibitors of matrix metalloproteinase activity (Metzincin family proteases). Consequently, TIMPs are often considered simply as protease inhibitors by many investigators. However, an evolving list of new metalloproteinase-independent functions for TIMP family members suggests that this concept is outdated. These novel TIMP functions include direct agonism/antagonism of multiple transmembrane receptors, as well as functional interactions with matrisome targets. While the family was fully identified over two decades ago, there has yet to be an in-depth study describing the expression of TIMPs in normal tissues of adult mammals. An understanding of the tissues and cell-types that express TIMPs 1 through 4, in both normal and disease states are important to contextualize the growing functional capabilities of TIMP proteins, which are often dismissed as non-canonical. Using publicly available single cell RNA sequencing data from the Tabula Muris Consortium, we analyzed approximately 100,000 murine cells across eighteen tissues from non-diseased organs, representing seventy-three annotated cell types, to define the diversity in Timp gene expression across healthy tissues. We describe the unique expression profiles across tissues and organ-specific cell types that all four Timp genes display. Within annotated cell-types, we identify clear and discrete cluster-specific patterns of Timp expression, particularly in cells of stromal and endothelial origins. RNA in-situ hybridization across four organs expands on the scRNA sequencing analysis, revealing novel compartments associated with individual Timp expression. These analyses emphasize a need for specific studies investigating the functional significance of Timp expression in the identified tissues and cell sub-types. This understanding of the tissues, specific cell types and microenvironment conditions in which Timp genes are expressed adds important physiological context to the growing array of novel functions for TIMP proteins.

Transcriptional activation of Jun and Fos members of the AP-1 complex is a conserved signature of immune aging that contributes to inflammaging.

Diverse mouse strains have different health and life spans, mimicking the diversity among humans. To capture conserved aging signatures, we studied long-lived C57BL/6J and short-lived NZO/HILtJ mouse strains by profiling transcriptomes and epigenomes of immune cells from peripheral blood and the spleen from young and old mice. Transcriptional activation of the AP-1 transcription factor complex, particularly Fos, Junb, and Jun genes, was the most significant and conserved aging signature across tissues and strains. ATAC-seq data analyses showed that the chromatin around these genes was more accessible with age and there were significantly more binding sites for these TFs with age across all studied tissues, targeting pro-inflammatory molecules including Il6. Age-related increases in binding sites of JUN and FOS factors were also conserved in human peripheral blood ATAC-seq data. Single-cell RNA-seq data from the mouse aging cell atlas Tabula Muris Senis showed that the expression of these genes increased with age in B, T, NK cells, and macrophages, with macrophages from old mice expressing these molecules more abundantly than other cells. Functional data showed that upon myeloid cell activation via poly(I:C), the levels of JUN protein and its binding activity increased more significantly in spleen cells from old compared to young mice. In addition, upon activation, old cells produced more IL6 compared to young cells. In sum, we showed that the aging-related transcriptional activation of Jun and Fos family members in AP-1 complex is conserved across immune tissues and long- and short-living mouse strains, possibly contributing to increased inflammation with age.

Network analysis of large-scale ImmGen and Tabula Muris datasets highlights metabolic diversity of tissue mononuclear phagocytes.

The diversity of mononuclear phagocyte (MNP) subpopulations across tissues is one of the key physiological characteristics of the immune system. Here, we focus on understanding the metabolic variability of MNPs through metabolic network analysis applied to three large-scale transcriptional datasets: we introduce (1) an ImmGen MNP open-source dataset of 337 samples across 26 tissues; (2) a myeloid subset of ImmGen Phase I dataset (202 MNP samples); and (3) a myeloid mouse single-cell RNA sequencing (scRNA-seq) dataset (51,364 cells) assembled based on Tabula Muris Senis. To analyze such large-scale datasets, we develop a network-based computational approach, genes and metabolites (GAM) clustering, for unbiased identification of the key metabolic subnetworks based on transcriptional profiles. We define 9 metabolic subnetworks that encapsulate the metabolic differences within MNP from 38 different tissues. Obtained modules reveal that cholesterol synthesis appears particularly active within the migratory dendritic cells, while glutathione synthesis is essential for cysteinyl leukotriene production by peritoneal and lung macrophages.

Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing.

Spatial transcriptomics extends single-cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as multiplexed error-robust fluorescence in situ hybridization (MERFISH) can achieve single-cell resolution, directly mapping single-cell identities to spatial positions. MERFISH produces a different data type than scRNA-seq, and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on the mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas and from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both the liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that MERFISH provides a quantitatively comparable method for single-cell gene expression and can identify cell types without the need for computational integration with scRNA-seq atlases.

Single Cell Transcriptomic Analysis Reveals Organ Specific Pericyte Markers and Identities.

Pericytes are mesenchymal-derived mural cells that wrap around capillaries and directly contact endothelial cells. Present throughout the body, including the cardiovascular system, pericytes are proposed to have multipotent cell-like properties and are involved in numerous biological processes, including regulation of vascular development, maturation, permeability, and homeostasis. Despite their physiological importance, the functional heterogeneity, differentiation process, and pathological roles of pericytes are not yet clearly understood, in part due to the inability to reliably distinguish them from other mural cell populations. Our study focused on identifying pericyte-specific markers by analyzing single-cell RNA sequencing data from tissue-specific mouse pericyte populations generated by the Tabula Muris Senis. We identified the mural cell cluster in murine lung, heart, kidney, and bladder that expressed either of two known pericyte markers, Cspg4 or Pdgfrb. We further defined pericytes as those cells that co-expressed both markers within this cluster. Single-cell differential expression gene analysis compared this subset with other clusters that identified potential pericyte marker candidates, including Kcnk3 (in the lung); Rgs4 (in the heart); Myh11 and Kcna5 (in the kidney); Pcp4l1 (in the bladder); and Higd1b (in lung and heart). In addition, we identified novel markers of tissue-specific pericytes and signaling pathways that may be involved in maintaining their identity. Moreover, the identified markers were further validated in Human Lung Cell Atlas and human heart single-cell RNAseq databases. Intriguingly, we found that markers of heart and lung pericytes in mice were conserved in human heart and lung pericytes. In this study, we, for the first time, identified specific pericyte markers among lung, heart, kidney, and bladder and reveal differentially expressed genes and functional relationships between mural cells.

Abstract A026: Altered immune landscape in aging lungs contributes to malignant evolution

Abstract Aging is a major risk factor in increased lung cancer incidence. While most research has focused on age-associated mutation accumulation to explain the late-life increase in cancer incidence, there are tissue environmental forces that both impede and promote cancer evolution. Just as organismal evolution is known to be driven by environmental changes, cellular (somatic) evolution in our bodies is similarly driven by changes in tissue environments. Environmental change promotes selection for new phenotypes that are adaptive to the new context. In our tissues, aging or insult-driven alterations in tissues drives selection for adaptive mutations, and some of these mutations can confer malignant phenotypes. Chronic, low-level inflammation has been associated with aging, termed inflammaging, yet how age-associated changes in lung tissue microenvironments contribute to increased lung cancer incidence has remained largely unknown. Since chronic inflammation has been shown to contribute to tumor development, we hypothesized that inflammaging contributes to increased oncogenic adaptation in the lung. Using either viral delivery of CRISPR constructs to mediate EML4-ALK translocations or ectopic expression of KRAS-G12D, we showed increased adenoma formation in old mice. Importantly, in the EML4-ALK model, we showed that the overexpression of alpha-1 antitrypsin (AAT) in old mice resulted in lower adenoma counts compared to their old wild type counterparts. Flow cytometric analysis of immune cells isolated from bronchoalveolar fluid of young and old mice showed an altered immune landscape, such as increased neutrophils, gamma delta T cells, and Foxp3+ regulatory T cells. Furthermore, analysis of the single-cell RNAseq data from Tabula Muris Consortium demonstrated increased exhaustion markers in the CD8+ T cells and regulatory T cells. Separately, Gene Set Enrichment Analysis (GSEA) of the differential gene expressions of lung epithelial cells isolated from young and old mice revealed enriched pathways related to immune activation and inflammatory response, and immune-suppression markers. Lastly, bulk RNA-seq from lungs of young, old, and old mice overexpressing AAT revealed increased immune cell exhaustion markers and that the overexpression of AAT partially reversed this increase. Finally, analysis of Genotype-Tissue Expression (GTEx) data comparing gene expressions in lungs of young and old humans similarly showed enriched pathways related to immune activation and increased T cell exhaustion markers in the elderly. In addition, using deconvolution methods CiberSort and xCell, we demonstrated altered innate and adaptive immune cell populations, for example, increased neutrophils and regulatory T cells, that are associated with advanced age, similar to aging mice. In conclusion, we showed that there is an exhausted immune microenvironment in aging lungs, that inflammation contributes to the increased tumor initiation, and that decreasing inflammation could decrease the lung tumor incidence by reactivating the immune system. Citation Format: Shi Biao Chia, Catherine Pham-Danis, Hannah Scarborough, Nathaniel Little, Etienne P. Danis, Andrew E. Goodspeed, Charles Dinarello, James DeGregori. Altered immune landscape in aging lungs contributes to malignant evolution [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A026.

Semantic clustering analysis of E3-ubiquitin ligases in gastrointestinal tract defines genes ontology clusters with tissue expression patterns

BackgroundUbiquitin ligases (Ub-ligases) are essential intracellular enzymes responsible for the regulation of proteome homeostasis, signaling pathway crosstalk, cell differentiation and stress responses. Individual Ub-ligases exhibit their unique functions based on the nature of their substrates. They create a complex regulatory network with alternative and feedback pathways to maintain cell homeostasis, being thus important players in many physiological and pathological conditions. However, the functional classification of Ub-ligases needs to be revised and extended.MethodsIn the current study, we used a novel semantic biclustering technique for expression profiling of Ub-ligases and ubiquitination-related genes in the murine gastrointestinal tract (GIT). We accommodated a general framework of the algorithm for finding tissue-specific gene expression clusters in GIT. In order to test identified clusters in a biological system, we used a model of epithelial regeneration. For this purpose, a dextran sulfate sodium (DSS) mouse model, following with in situ hybridization, was used to expose genes with possible compensatory features. To determine cell-type specific distribution of Ub-ligases and ubiquitination-related genes, principal component analysis (PCA) and Uniform Manifold Approximation and Projection technique (UMAP) were used to analyze the Tabula Muris scRNA-seq data of murine colon followed by comparison with our clustering results.ResultsOur established clustering protocol, that incorporates the semantic biclustering algorithm, demonstrated the potential to reveal interesting expression patterns. In this manner, we statistically defined gene clusters consisting of the same genes involved in distinct regulatory pathways vs distinct genes playing roles in functionally similar signaling pathways. This allowed us to uncover the potentially redundant features of GIT-specific Ub-ligases and ubiquitination-related genes. Testing the statistically obtained results on the mouse model showed that genes clustered to the same ontology group simultaneously alter their expression pattern after induced epithelial damage, illustrating their complementary role during tissue regeneration.ConclusionsAn optimized semantic clustering protocol demonstrates the potential to reveal a readable and unique pattern in the expression profiling of GIT-specific Ub-ligases, exposing ontologically relevant gene clusters with potentially redundant features. This extends our knowledge of ontological relationships among Ub-ligases and ubiquitination-related genes, providing an alternative and more functional gene classification. In a similar way, semantic cluster analysis could be used for studding of other enzyme families, tissues and systems.

Robust and annotation-free analysis of alternative splicing across diverse cell types in mice

Although alternative splicing is a fundamental and pervasive aspect of gene expression in higher eukaryotes, it is often omitted from single-cell studies due to quantification challenges inherent to commonly used short-read sequencing technologies. Here, we undertake the analysis of alternative splicing across numerous diverse murine cell types from two large-scale single-cell datasets-the Tabula Muris and BRAIN Initiative Cell Census Network-while accounting for understudied technical artifacts and unannotated events. We find strong and general cell-type-specific alternative splicing, complementary to total gene expression but of similar discriminatory value, and identify a large volume of novel splicing events. We specifically highlight splicing variation across different cell types in primary motor cortex neurons, bone marrow B cells, and various epithelial cells, and we show that the implicated transcripts include many genes which do not display total expression differences. To elucidate the regulation of alternative splicing, we build a custom predictive model based on splicing factor activity, recovering several known interactions while generating new hypotheses, including potential regulatory roles for novel alternative splicing events in critical genes like Khdrbs3 and Rbfox1. We make our results available using public interactive browsers to spur further exploration by the community.

Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data

BackgroundA key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods.ResultsWe systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data.ConclusionsWe identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from (https://github.com/PYangLab/scCCESS).

KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem.

De novo pathway enrichment is a systems biology approach in which OMICS data are projected onto a molecular interaction network to identify subnetworks representing condition-specific functional modules and molecular pathways. Compared to classical pathway enrichment analysis methods, de novo pathway enrichment is not limited to predefined lists of pathways from (curated) databases and thus particularly suited for discovering novel disease mechanisms. While several tools have been proposed for pathway enrichment, the integration of de novo pathway enrichment in end-to-end OMICS analysis workflows in the R programming language is currently limited to a single tool. To close this gap, we have implemented an R package KeyPathwayMineR (KPM-R). The package extends the features and usability of existing versions of KeyPathwayMiner by leveraging the power, flexibility and versatility of R and by providing various novel functionalities for performing data preparation, visualization, and comparison. In addition, thanks to its interoperability with a plethora of existing R packages in e.g., Bioconductor, CRAN, and GitHub, KPM-R allows carrying out the initial preparation of the datasets and to meaningfully interpret the extracted subnetworks. To demonstrate the package’s potential, KPM-R was applied to bulk RNA-Seq data of nasopharyngeal swabs from SARS-CoV-2 infected individuals, and on single cell RNA-Seq data of aging mice tissue from the Tabula Muris Senis atlas.