Protocol for analysis of single-cell sequencing data by Seqtometry.

Seqtometry (sequencing-to-measurement) is an analytical platform for single-cell analysis based on direct profiling of gene expression and accessibility achieved by advanced scoring with gene signatures. Here, we present a protocol for single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis using Seqtometry. We describe steps for preprocessing, imputation, scoring, and plotting, with extensions to large datasets and integration of multiple datasets. This protocol yields results in the form of biologically interpretable dimensions for direct identification and comprehensive characterization of specific cells. For complete details on the use and execution of this protocol, please refer to Kousnetsov et al.1.

Genome-wide expression analysis in a Fabry disease human podocyte cell line.

Fabry disease (FD) is an X-linked lysosomal disease caused by an enzyme deficiency of alpha-galactosidase A (α-gal A). This deficiency leads to the accumulation of glycosphingolipids in lysosomes, resulting in a range of clinical symptoms. The complex pathogenesis of FD involves lysosomal dysfunction, altered autophagy, and mitochondrial abnormalities. Omics sciences, particularly transcriptomic analysis, comprehensively understand molecular mechanisms underlying diseases. This study focuses on genome-wide expression analysis in an FD human podocyte model to gain insights into the underlying mechanisms of podocyte dysfunction. Human control and GLA-edited podocytes were used. Gene expression data was generated using RNA-seq analysis, and differentially expressed genes were identified using DESeq2. Principal component analysis and Spearman correlation have explored gene expression trends. Functional enrichment and Reporter metabolite analyses were conducted to identify significantly affected metabolites and metabolic pathways. Differential expression analysis revealed 247 genes with altered expression levels in GLA-edited podocytes compared to control podocytes. Among these genes, 136 were underexpressed, and 111 were overexpressed in GLA-edited cells. Functional analysis of differentially expressed genes showed their involvement in various pathways related to oxidative stress, inflammation, fatty acid metabolism, collagen and extracellular matrix homeostasis, kidney injury, apoptosis, autophagy, and cellular stress response. The study provides insights into molecular mechanisms underlying Fabry podocyte dysfunction. Integrating transcriptomics data with genome-scale metabolic modeling further unveiled metabolic alterations in GLA-edited podocytes. This comprehensive approach contributes to a better understanding of Fabry disease and may lead to identifying new biomarkers and therapeutic targets for this rare lysosomal disorder.

Proteomic dynamics revealed sex-biased responses to combined heat-drought stress in Marchantia.

Recent studies have documented plant responses to climate change extensively, particularly to single-stress exposures. However, critical factors for stress survival, such as sexual differentiation, are not often considered. The dioicous Marchantia polymorpha stands as an evolutionary milestone, potentially preserving ancestral traits from the early colonizers. In this study, we employed proteomic analyses complemented with physiological monitoring to investigate combined heat and drought responses in Tak-1 (male) and Tak-2 (female) accessions of this liverwort. Additionally, targeted transcriptomics was conducted using different natural populations from contrasting environments. Our findings revealed sex-biased dynamics among natural accessions, particularly evident under control conditions and during early stress responses. Although Tak-2 exhibited greater diversity than Tak-1 under control conditions, male accession demonstrated distinct and more rapid stress sensing and signaling. These differences in stress response appeared to be strongly related to sex-specific plasticity influenced by geoclimatic origin. Furthermore, we established distinct protein gene ages and genomic distribution trends, underscoring the importance of protein diversification over time. This study provides an evolutionary perspective on sexual divergence and stress emergence employing a systems biology approach, which allowed for the establishment of global and sex-specific interaction networks in the stress response.

Cisplatin resistance-related transcriptome and methylome integration identifies PCDHB4 as a novel prognostic biomarker in small cell lung cancer.

Platinum-based chemo-resistance is the major issue for the treatment of small cell lung cancer (SCLC). The integrative analysis of multi-omics data is a reliable approach for discovering novel biomarkers associated with chemo-resistance. Here, multi-omics integrative analysis and Cox regression found that higher expression of PCDHB4 was associated with poorer survival of SCLC patients who received chemotherapy. PCDHB4 gene was hypomethylated and upregulated in SCLC, which was validated in the levels of promoter methylation, mRNA, and protein expression. Mechanistically, using bulk RNA-seq data, functional enrichment analysis indicated that higher PCDHB4 expression was associated with lower immune infiltration. The analysis of single-cell RNA-seq (scRNA-seq) found that SCLC cells with PCDHB4 expression exhibited the characteristics of stemness and EMT. In addition, the high expression and hypomethylation of PCDHB4 were also significantly associated with poor survival in lung squamous cell carcinoma. In summary, PCDHB4 is a potential prognostic biomarker of platinum-based chemotherapy in SCLC.

Loss of ZNF408 attenuates STING-mediated immune surveillance in breast carcinogenesis.

Understanding the mechanism of cancer immune surveillance is crucial for precision medicine and effective immunotherapy. We report here that ZNF408, encoded by a gene linked to familial exudative vitreoretinopathy (FEVR) and autosomal recessive retinitis pigmentosa (RP), is physically associated with the SETD1A/COMPASS complex mediating histone H3 lysine 4 (H3K4) methylation in breast cancer cells. Integrative epigenomic and transcriptomic analyses reveal that ZNF408 and SETD1A share overlapped chromatin landscape and coordinately activate a cohort of genes, among which STING1 is critical in innate immune responses. ZNF408-SETD1A complex enhances STING1 expression and promotes STING-mediated anti-tumor immune responses both in vitro and in vivo. Importantly, ZNF408 expression is positively correlated with that of STING1 and negatively correlated with the histological grade of breast cancer. Our study uncovers a role for ZNF408 in cancer immune surveillance, supporting further investigations for therapeutic targeting of ZNF408-SETD1A-STING1 axis in breast carcinogenesis and other ZNF408-associated diseases including FEVR and RP.

Kiphynet: an online network simulation tool connecting cellular kinetics and physiological transport.

INTRODUCTION: Human metabolism is sustained by functional networks that operate at diverse scales. Capturing local and global dynamics in the human body by hierarchically bridging multi-scale functional networks is a major challenge in physiological modeling. OBJECTIVES: To develop an interactive, user-friendly web application that facilitates the simulation and visualization of advection-dispersion transport in three-dimensional (3D) microvascular networks, biochemical exchange, and metabolic reactions in the tissue layer surrounding the vasculature. METHODS: To help modelers combine and simulate biochemical processes occurring at multiple scales, KiPhyNet deploys our discrete graph-based modeling framework that bridges functional networks existing at diverse scales. KiPhyNet is implemented in Python based on Apache web server using MATLAB as the simulator engine. KiPhyNet provides the functionality to assimilate multi-omics data from clinical and experimental studies as well as vascular data from imaging studies to investigate the role of structural changes in vascular topology on the functional response of the tissue. RESULTS: With the network topology, its biophysical attributes, values of initial and boundary conditions, parameterized kinetic constants, biochemical species-specific transport properties such as diffusivity as inputs, a user can use our application to simulate and view the simulation results. The results of steady-state velocity and pressure fields and dynamic concentration fields can be interactively examined. CONCLUSION: KiPhyNet provides barrier-free access to perform time-course simulation experiments by building multi-scale models of microvascular networks in physiology, using a discrete modeling framework. KiPhyNet is freely accessible at   http://pallab.cds.iisc.ac.in/kiphynet/ and the documentation is available at   https://deepamahm.github.io/kiphynet_docs/ .

Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models.

Utilization of 16S rRNA data in constraint-based modeling to characterize microbial communities confronts a major hurdle of lack of species-level resolution, impeding the construction of community models. We introduce "Panera," an innovative framework designed to model communities under this uncertainty and yet perform metabolic inferences using pan-genus metabolic models (PGMMs). We demonstrated PGMMs' utility for comprehending the metabolic capabilities of a genus and in characterizing community models using amplicon data. The unique, adaptable nature of PGMMs unlocks their potential in building hybrid communities, combining genome-scale metabolic models (GSMMs) and PGMMs. Notably, these models provide predictions comparable to the standard GSMM-based community models, while achieving a nearly 46% reduction in error compared to the genus model-based communities. In essence, "Panera" presents a potent and effective approach to aid in metabolic modeling by enabling robust predictions of community metabolic potential when dealing with amplicon data, and offers insights into genus-level metabolic landscapes.

Systems biology-based analysis exploring shared biomarkers and pathogenesis of myocardial infarction combined with osteoarthritis.

Background: More and more evidence supports the association between myocardial infarction (MI) and osteoarthritis (OA). The purpose of this study is to explore the shared biomarkers and pathogenesis of MI complicated with OA by systems biology. Methods: Gene expression profiles of MI and OA were downloaded from the Gene Expression Omnibus (GEO) database. The Weighted Gene Co-Expression Network Analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to identify the common DEGs. The shared genes related to diseases were screened by three public databases, and the protein-protein interaction (PPI) network was built. GO and KEGG enrichment analyses were performed on the two parts of the genes respectively. The hub genes were intersected and verified by Least absolute shrinkage and selection operator (LASSO) analysis, receiver operating characteristic (ROC) curves, and single-cell RNA sequencing analysis. Finally, the hub genes differentially expressed in primary cardiomyocytes and chondrocytes were verified by RT-qPCR. The immune cell infiltration analysis, subtypes analysis, and transcription factors (TFs) prediction were carried out. Results: In this study, 23 common DEGs were obtained by WGCNA and DEGs analysis. In addition, 199 common genes were acquired from three public databases by PPI. Inflammation and immunity may be the common pathogenic mechanisms, and the MAPK signaling pathway may play a key role in both disorders. DUSP1, FOS, and THBS1 were identified as shared biomarkers, which is entirely consistent with the results of single-cell RNA sequencing analysis, and furher confirmed by RT-qPCR. Immune infiltration analysis illustrated that many types of immune cells were closely associated with MI and OA. Two potential subtypes were identified in both datasets. Furthermore, FOXC1 may be the crucial TF, and the relationship of TFs-hub genes-immune cells was visualized by the Sankey diagram, which could help discover the pathogenesis between MI and OA. Conclusion: In summary, this study first revealed 3 (DUSP1, FOS, and THBS1) novel shared biomarkers and signaling pathways underlying both MI and OA. Additionally, immune cells and key TFs related to 3 hub genes were examined to further clarify the regulation mechanism. Our study provides new insights into shared molecular mechanisms between MI and OA.

Prelude to a Compositional Systems Biology.

Composition is a powerful principle for systems biology, focused on the interfaces, interconnections, and orchestration of distributed processes. Whereas most systems biology models focus on the structure or dynamics of specific subsystems in controlled conditions, compositional systems biology aims to connect such models into integrative multiscale simulations. This emphasizes the space between models-a compositional perspective asks what variables should be exposed through a submodel's interface? How do coupled models connect and translate across scales? How can we connect domain-specific models across biological and physical research areas to drive the synthesis of new knowledge? What is required of software that integrates diverse datasets and submodels into unified multiscale simulations? How can the resulting integrative models be accessed, flexibly recombined into new forms, and iteratively refined by a community of researchers? This essay offers a high-level overview of the key components for compositional systems biology, including: 1) a conceptual framework and corresponding graphical framework to represent interfaces, composition patterns, and orchestration patterns; 2) standardized composition schemas that offer consistent formats for composable data types and models, fostering robust infrastructure for a registry of simulation modules that can be flexibly assembled; 3) a foundational set of biological templates-schemas for cellular and molecular interfaces, which can be filled with detailed submodels and datasets, and are designed to integrate knowledge that sheds light on the molecular emergence of cells; and 4) scientific collaboration facilitated by user-friendly interfaces for connecting researchers with datasets and models, and which allows a community of researchers to effectively build integrative multiscale models of cellular systems.

Combining mutation and recombination statistics to infer clonal families in antibody repertoires.

B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution and dynamics. We present HILARy (High-precision Inference of Lineages in Antibody Repertoires), an efficient, fast and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and 𝑑𝑁∕𝑑𝑆 ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.

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.

Discovery and generalization of tissue structures from spatial omics data.

Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.

Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics.

The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.

Omics-derived biological modules reflect metabolic brain changes in Alzheimer's disease.

INTRODUCTION: Brain glucose hypometabolism, indexed by the fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) imaging, is a metabolic signature of Alzheimer's disease (AD). However, the underlying biological pathways involved in these metabolic changes remain elusive. METHODS: Here, we integrated [18F]FDG-PET images with blood and hippocampal transcriptomic data from cognitively unimpaired (CU, n = 445) and cognitively impaired (CI, n = 749) individuals using modular dimension reduction techniques and voxel-wise linear regression analysis. RESULTS: Our results showed that multiple transcriptomic modules are associated with brain [18F]FDG-PET metabolism, with the top hits being a protein serine/threonine kinase activity gene cluster (peak-t(223) = 4.86, P value < 0.001) and zinc-finger-related regulatory units (peak-t(223) = 3.90, P value < 0.001). DISCUSSION: By integrating transcriptomics with PET imaging data, we identified that serine/threonine kinase activity-associated genes and zinc-finger-related regulatory units are highly associated with brain metabolic changes in AD. HIGHLIGHTS: We conducted an integrated analysis of system-based transcriptomics and fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) at the voxel level in Alzheimer's disease (AD). The biological process of serine/threonine kinase activity was the most associated with [18F]FDG-PET in the AD brain. Serine/threonine kinase activity alterations are associated with brain vulnerable regions in AD [18F]FDG-PET. Zinc-finger transcription factor targets were associated with AD brain [18F]FDG-PET metabolism.

SkinCom, a synthetic skin microbial community, enables reproducible investigations of the human skin microbiome.

Existing models of the human skin have aided our understanding of skin health and disease. However, they currently lack a microbial component, despite microbes' demonstrated connections to various skin diseases. Here, we present a robust, standardized model of the skin microbial community (SkinCom) to support in vitro and in vivo investigations. Our methods lead to the formation of an accurate, reproducible, and diverse community of aerobic and anaerobic bacteria. Subsequent testing of SkinCom on the dorsal skin of mice allowed for DNA and RNA recovery from both the applied SkinCom and the dorsal skin, highlighting its practicality for in vivo studies and -omics analyses. Furthermore, 66% of the responses to common cosmetic chemicals in vitro were in agreement with a human trial. Therefore, SkinCom represents a valuable, standardized tool for investigating microbe-metabolite interactions and facilitates the experimental design of in vivo studies targeting host-microbe relationships.

Nova-ST: Nano-patterned ultra-dense platform for spatial transcriptomics.

Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large-scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low-cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods at a reduced cost.

Non-dialyzable uremic toxins and renal tubular cell damage in CKD patients: a systems biology approach.

BACKGROUND: Chronic kidney disease presents global health challenges, with hemodialysis as a common treatment. However, non-dialyzable uremic toxins demand further investigation for new therapeutic approaches. Renal tubular cells require scrutiny due to their vulnerability to uremic toxins. METHODS: In this study, a systems biology approach utilized transcriptomics data from healthy renal tubular cells exposed to healthy and post-dialysis uremic plasma. RESULTS: Differential gene expression analysis identified 983 up-regulated genes, including 70 essential proteins in the protein-protein interaction network. Modularity-based clustering revealed six clusters of essential proteins associated with 11 pathological pathways activated in response to non-dialyzable uremic toxins. CONCLUSIONS: Notably, WNT1/11, AGT, FGF4/17/22, LMX1B, GATA4, and CXCL12 emerged as promising targets for further exploration in renal tubular pathology related to non-dialyzable uremic toxins. Understanding the molecular players and pathways linked to renal tubular dysfunction opens avenues for novel therapeutic interventions and improved clinical management of chronic kidney disease and its complications.

Linking coronary artery disease to neurodegenerative diseases through systems genetics.

Coronary artery disease (CAD) is still a leading cause of death worldwide despite the extensive research and the considerable progresses made through the years. As other cardiovascular diseases, CAD is the result of the complex interaction between genetic variants and environmental factors. Currently identified genetic loci associated to CAD revealed the contribution of multiple molecular pathways to its pathogenesis, suggesting the need for a systemic approach to understand the role of genetic determinants. In this study we wanted to investigate how GWAS variants associated to CAD interact with each other and with nearby genes in the context of the coronary artery molecular interactome. GWAS variants associated to CAD were selected from GWAS Catalog, then, a tissue-specific interactome was constructed integrating protein-protein interactions (PPI) from multiple public repositories and computationally inferred co-expression relationships. To focus on the part of the network most relevant for CAD, we selected the interactions connecting the genes carrying a variant associated to the disease. A functional enrichment analysis conducted on the subnetwork revealed that genes carrying genetic variants associated to CAD closely interact with genes related to relevant biological processes, such as extracellular matrix organization, lipoprotein clearance, arterial morphology and inflammatory response. These results confirm that the identified subnetwork reflects the molecular pathways altered in CAD and intercepted by the selected variants. Interestingly, the most connected nodes of the network included amyloid beta precursor protein (APP) and huntingtin (HTT), both implicated in neurodegenerative disorders. In recent years the interest in investigating the common processes between cardiovascular diseases and neurodegenerative disorders is increasing, with growing evidence of a link between CAD and Alzheimer's disease. The results obtained in this work support the association between such apparently unrelated diseases and highlight the necessity of a systems biology approach to better elucidate shared pathological mechanisms.

A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets.

Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern Next-Generation Sequencing data. We here present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.

TGF-ß neutralization attenuates tumor residency of activated T cells to enhance systemic immunity in mice.

A tissue resident-like phenotype in tumor infiltrating T cells can limit systemic anti-tumor immunity. Enhanced systemic anti-tumor immunity is observed in head and neck cancer patients after neoadjuvant PD-L1 immune checkpoint blockade (ICB) and transforming growth factor ß (TGF-ß) neutralization. Using T cell receptor (TCR) sequencing and functional immunity assays in a syngeneic model of oral cancer, we dissect the relative contribution of these treatments to enhanced systemic immunity. The addition of TGF-ß neutralization to ICB resulted in the egress of expanded and exhausted CD8+ tumor infiltrating lymphocytes (TILs) into circulation and greater systemic anti-tumor immunity. This enhanced egress associated with reduced expression of Itgae (CD103) and its upstream regulator Znf683. Circulating CD8+ T cells expressed higher Cxcr3 after treatment, an observation also made in samples from patients treated with dual TGF-ß neutralization and ICB. These findings provide the scientific rationale for the use of PD-L1 ICB and TGF-ß neutralization in newly diagnosed patients with carcinomas prior to definitive treatment of locoregional disease.