Identification of a Novel ECM Remodeling Macrophage Subset in AKI to CKD Transition by Integrative Spatial and Single-Cell Analysis.

The transition from acute kidney injury (AKI) to chronic kidney disease (CKD) is a critical clinical issue. Although previous studies have suggested macrophages as a key player in promoting inflammation and fibrosis during this transition, the heterogeneity and dynamic characterization of macrophages are still poorly understood. Here, we used integrated single-cell RNA sequencing and spatial transcriptomic to characterize the spatiotemporal heterogeneity of macrophages in murine AKI-to-CKD model of unilateral ischemia-reperfusion injury. A marked increase in macrophage infiltration at day 1 was followed by a second peak at day 14 post AKI. Spatiotemporal profiling revealed that injured tubules and macrophages co-localized early after AKI, whereas in late chronic stages had spatial proximity to fibroblasts. Further pseudotime analysis revealed two distinct lineages of macrophages in this transition: renal resident macrophages differentiated into the pro-repair subsets, whereas infiltrating monocyte-derived macrophages contributed to chronic inflammation and fibrosis. A novel macrophage subset, extracellular matrix remodeling-associated macrophages (EAMs) originating from monocytes, linked to renal fibrogenesis and communicated with fibroblasts via insulin-like growth factors (IGF) signalling. In sum, our study identified the spatiotemporal dynamics of macrophage heterogeneity with a unique subset of EAMs in AKI-to-CKD transition, which could be a potential therapeutic target for preventing CKD development.

Stellate cells are in utero markers of pancreatic disease in cystic fibrosis.

BACKGROUND: Pancreatic fibrosis is an early diagnostic feature of the common inherited disorder cystic fibrosis (CF). Many people with CF (pwCF) are pancreatic insufficient from birth and the replacement of acinar tissue with cystic lesions and fibrosis is a progressive phenotype that may later lead to diabetes. Little is known about the initiating events in the fibrotic process though it may be a sequela of inflammation in the pancreatic ducts resulting from loss of CFTR impairing normal fluid secretion. Here we use a sheep model of CF (CFTR-/-) to examine the evolution of pancreatic disease through gestation. METHODS: Fetal pancreas was collected at six time points from 50-days of gestation through to term, which is equivalent to ~ 13 weeks to term in human. RNA was extracted from tissue for bulk RNA-seq and single cells were prepared from 80-day, 120-day and term samples for scRNA-seq. Data were validated by immunochemistry. RESULTS: Transcriptomic evidence from bulk RNA-seq showed alterations in the CFTR-/- pancreas by 65-days of gestation, which are accompanied by marked pathological changes by 80-days of gestation. These include a fibrotic response, confirmed by immunostaining for COL1A1, αSMA and SPARC, together with acinar loss. Moreover, using scRNA-seq we identify a unique cell population that is significantly overrepresented in the CFTR-/- animals at 80- and 120-days gestation, as are stellate cells at term. CONCLUSION: The transcriptomic changes and cellular imbalance that we observe likely have pivotal roles in the evolution of CF pancreatic disease and may provide therapeutic opportunities to delay or prevent pancreatic destruction in CF.

Disrupting YAP1-mediated glutamine metabolism induces synthetic lethality alongside ODC1 inhibition in osteosarcoma.

PURPOSE: Osteosarcoma, a highly malignant primary bone tumor primarily affecting adolescents, frequently develops resistance to initial chemotherapy, leading to metastasis and limited treatment options. Our study aims to uncover novel therapeutic targets for metastatic and recurrent osteosarcoma. METHODS: In this study, we proved the potential of modulating the YAP1-regulated glutamine metabolic pathway to augment the response of OS to DFMO. We initially employed single-cell transcriptomic data to gauge the activation level of polyamine metabolism in MTAP-deleted OS patients. This was further substantiated by transcriptome sequencing data from recurrent and non-recurrent patient tissues, confirming the activation of polyamine metabolism in progressive OS. Through high-throughput drug screening, we pinpointed CIL56, a YAP1 inhibitor, as a promising candidate for a combined therapeutic strategy with DFMO. In vivo, we utilized PDX and CDX models to validate the therapeutic efficacy of this drug combination. In vitro, we conducted western blot analysis, qPCR analysis, immunofluorescence staining, and PuMA experiments to monitor alterations in molecular expression, distribution, and tumor metastasis capability. We employed CCK-8 and colony formation assays to assess the proliferative capacity of cells in the experimental group. We used flow cytometry and reactive oxygen probes to observe changes in ROS and glutamine metabolism within the cells. Finally, we applied RNA-seq in tandem with metabolomics to identify metabolic alterations in OS cells treated with a DFMO and CIL56 combination. This enabled us to intervene and validate the role of the YAP1-mediated glutamine metabolic pathway in DFMO resistance. RESULTS: Through single-cell RNA-seq data analysis, we pinpointed a subset of late-stage OS cells with significantly upregulated polyamine metabolism. This upregulation was further substantiated by transcriptomic profiling of recurrent and non-recurrent OS tissues. High-throughput drug screening revealed a promising combination strategy involving DFMO and CIL56. DFMO treatment curbs the phosphorylation of YAP1 protein in OS cells, promoting nuclear entry and initiating the YAP1-mediated glutamine metabolic pathway. This reduces intracellular ROS levels, countering DFMO's anticancer effect. The therapeutic efficacy of DFMO can be amplified both in vivo and in vitro by combining it with the YAP1 inhibitor CIL56 or the glutaminase inhibitor CB-839. This underscores the significant potential of targeting the YAP1-mediated glutamine metabolic pathway to enhance efficacy of DFMO. CONCLUSION: Our findings elucidate YAP1-mediated glutamine metabolism as a crucial bypass mechanism against DFMO, following the inhibition of polyamine metabolism. Our study provides valuable insights into the potential role of DFMO in an "One-two Punch" therapy of metastatic and recurrent osteosarcoma.

Erratum: Extensive patient-to-patient single nucleus transcriptome heterogeneity in pheochromocytomas and paragangliomas.

[This corrects the article DOI: 10.3389/fonc.2022.965168.].

Luteinizing Hormone Receptor Mutation (LHRN316S) Causes Abnormal Follicular Development Revealed by Follicle Single-Cell Analysis and CRISPR/Cas9.

Abnormal interaction between granulosa cells and oocytes causes disordered development of ovarian follicles. However, the interactions between oocytes and cumulus granulosa cells (CGs), oocytes and mural granulosa cells (MGs), and CGs and MGs remain to be fully explored. Using single-cell RNA-sequencing (scRNA-seq), we determined the transcriptional profiles of oocytes, CGs and MGs in antral follicles. Analysis of scRNA-seq data revealed that CGs may regulate follicular development through the BMP15-KITL-KIT-PI3K-ARF6 pathway with elevated expression of luteinizing hormone receptor (LHR). Because internalization of the LHR is regulated by Arf6, we constructed LHRN316S mice by CRISPR/Cas9 to further explore mechanisms of follicular development and novel treatment strategies for female infertility. Ovaries of LHRN316S mice exhibited reduced numbers of corpora lutea and ovulation. The LHRN316S mice had a reduced rate of oocyte maturation in vitro and decreased serum progesterone levels. Mating LHRN316S female mice with ICR wild type male mice revealed that the infertility rate of LHRN316S mice was 21.4% (3/14). Litter sizes from LHRN316S mice were smaller than those from control wild type female mice. The oocytes from LHRN316S mice had an increased rate of maturation in vitro after progesterone administration in vitro. Furthermore, progesterone treated LHRN316S mice produced offspring numbers per litter equivalent to WT mice. These findings provide key insights into cellular interactions in ovarian follicles and provide important clues for infertility treatment.

Transcriptional noise, gene activation, and roles of SAGA and Mediator Tail measured using nucleotide recoding single-cell RNA-seq.

We describe a time-resolved nascent single-cell RNA sequencing (RNA-seq) approach that measures gene-specific transcriptional noise and the fraction of active genes in S. cerevisiae. Most genes are expressed with near-constitutive behavior, while a subset of genes show high mRNA variance suggestive of transcription bursting. Transcriptional noise is highest in the cofactor/coactivator-redundant (CR) gene class (dependent on both SAGA and TFIID) and strongest in TATA-containing CR genes. Using this approach, we also find that histone gene transcription switches from a low-level, low-noise constitutive mode during M and M/G1 to an activated state in S phase that shows both an increase in the fraction of active promoters and a switch to a noisy and bursty transcription mode. Rapid depletion of cofactors SAGA and MED Tail indicates that both factors play an important role in stimulating the fraction of active promoters at CR genes, with a more modest role in transcriptional noise.

Hepatic BMAL1 and HIF1α regulate a time-dependent hypoxic response and prevent hepatopulmonary-like syndrome.

The transcriptional response to hypoxia is temporally regulated, yet the molecular underpinnings and physiological implications are unknown. We examined the roles of hepatic Bmal1 and Hif1α in the circadian response to hypoxia in mice. We found that the majority of the transcriptional response to hypoxia is dependent on either Bmal1 or Hif1α, through shared and distinct roles that are daytime determined. We further show that hypoxia-inducible factor (HIF)1α accumulation upon hypoxia is temporally regulated and Bmal1 dependent. Unexpectedly, mice lacking both hepatic Bmal1 and Hif1α are hypoxemic and exhibit increased mortality upon hypoxic exposure in a daytime-dependent manner. These mice display mild liver dysfunction with pulmonary vasodilation likely due to extracellular signaling regulated kinase (ERK) activation, endothelial nitric oxide synthase, and nitric oxide accumulation in lungs, suggestive of hepatopulmonary syndrome. Our findings indicate that hepatic BMAL1 and HIF1α are key time-dependent regulators of the hypoxic response and can provide molecular insights into the pathophysiology of hepatopulmonary syndrome.

A Low Expression of NRF2 Enhances Oxidative Stress and Autophagy in Myofibroblasts, Promoting Progression of Chronic Obstructive Pulmonary Disease.

BACKGROUND: The inflammation phenotypes are often closely related to oxidative stress and autophagy pathway activation, which could be developed as a treatment target. AIMS: The aim of this study was to explore the underlying mechanism of inflammation in chronic obstructive pulmonary disease (COPD). METHODS: The lung tissue single-cell RNA-seq (scRNA-seq) dataset of GSE171541 was downloaded from the Gene Expression Omnibus (GEO) database. The marker genes were obtained from the CellMarker database. "Seurat" and "harmony" R packages were used for single-cell profiling analysis. Then, the "AUCell" R package was employed to calculate the reactive oxygen species (ROS) and autophagy pathway scores. Gene regulation network analysis was performed by applying the "SCENIC" package, followed by conducting correlation analysis with Spearman's rank correlation method. The cigarettes were used to develop a traumatic model in mice, and the expression of relevant genes was measured by qRT-PCR. RESULTS: The scRNA-seq analysis classified 12 cell subgroups in which the contractility of myofibroblasts was closely associated with the progression of COPD. Further analysis showed that ROS and autophagy pathways were significantly activated in myofibroblasts and that the nuclear factor erythroid 2-related factor 2 (NRF2) and its mediated oxidative stress pathway were inhibited in myofibroblasts. In addition, the downregulated NRF2 gene was negatively correlated with the expression of autophagy and ROS activation. In the traumatic mice model, NRF2 was downregulated in COPD mice but further elevated in the COPD+NRF2 mice group. Interestingly, the mRNA levels of Kelchlike ECH-associated protein 1 (Keap1), NADPH oxidase (NOX), and Cathepsin B (CTSB) were upregulated in COPD group in comparison to the control group but they were downregulated by NRF2. These results suggested that low-expressed NFR2 promoted autophagy and ROS pathway activation in myofibroblasts for COPD progression. CONCLUSION: We identified a cell myofibroblast cluster closely associated with COPD progression using the scRNA-seq analysis. The downregulated NFR2, as a key risk factor, mediated myofibroblast death by activating the oxidative stress and autophagy pathway for COPD progression.

A human fetal cerebellar map of the late second trimester reveals developmental molecular characteristics and abnormality in trisomy 21.

Our understanding of human fetal cerebellum development during the late second trimester, a critical period for the generation of astrocytes, oligodendrocytes, and unipolar brush cells (UBCs), remains limited. Here, we performed single-cell RNA sequencing (scRNA-seq) in human fetal cerebellum samples from gestational weeks (GWs) 18-25. We find that proliferating UBC progenitors distribute in the subventricular zone of the rhombic lip (RLSVZ) near white matter (WM), forming a layer structure. We also delineate two trajectories from astrogenic radial glia (ARGs) to Bergmann glial progenitors (BGPs) and recognize oligodendrogenic radial glia (ORGs) as one source of primitive oligodendrocyte progenitor cells (PriOPCs). Additionally, our scRNA-seq analysis of the trisomy 21 fetal cerebellum at this stage reveals abnormal upregulated genes in pathways such as the cell adhesion pathway and focal adhesion pathway, which potentially promote neuronal differentiation. Overall, our research provides valuable insights into normal and abnormal development of the human fetal cerebellum.

Unsupervised Single-Cell Clustering with Asymmetric Within-Sample Transformation and Per-Cluster Supervised Features Selection.

This chapter shows applying the Asymmetric Within-Sample Transformation to single-cell RNA-Seq data matched with a previous dropout imputation. The asymmetric transformation is a special winsorization that flattens low-expressed intensities and preserves highly expressed gene levels. Before a standard hierarchical clustering algorithm, an intermediate step removes noninformative genes according to a threshold applied to a per-gene entropy estimate. Following the clustering, a time-intensive algorithm is shown to uncover the molecular features associated with each cluster. This step implements a resampling algorithm to generate a random baseline to measure up/downregulated significant genes. To this aim, we adopt a GLM model as implemented in DESeq2 package. We render the results in graphical mode. While the tools are standard heat maps, we introduce some data scaling to clarify the results' reliability.

Zebrafish Thrombocyte Transcriptome Analysis and Functional Genomics.

Our laboratory is interested in investigating the maturation process of zebrafish thrombocytes, which are functional equivalents to human platelets. We have adopted the zebrafish model to gain insights into mammalian platelet production, or thrombopoiesis. Notably, zebrafish exhibit two distinct populations of thrombocytes in their circulating blood: young and mature thrombocytes. This observation is intriguing because maturation appears to occur in circulation, yet the precise mechanisms governing this maturation remain elusive. Our goal is to understand the mechanisms underlying thrombocyte maturation by conducting single-cell RNA sequencing (scRNA-Seq) on young and mature thrombocytes, analyzing these transcriptomes to identify genes specific to each thrombocyte population, and elucidating the role of these genes in the maturation process, by quantifying thrombocyte numbers after the piggyback knockdown of each of these genes. In this chapter, we present a comprehensive, step-by-step protocol detailing the multifaceted methodology involved in understanding thrombocyte maturation, which encompasses the collection of zebrafish blood, the separation of young and mature thrombocytes using flow cytometry, scRNA-Seq analysis of these distinct thrombocyte populations, identification of genes specific to young and mature thrombocytes, and subsequent validation through gene knockdown techniques.

Inferring Novel Cells in Single-Cell RNA-Sequencing Data.

Single-cell RNA-sequencing (scRNA-seq) is a powerful technology that allows researchers to study gene expression heterogeneity within a tissue or cell population. One of the major advantages of scRNA-seq is that it allows researchers to identify and characterize novel cell types or subpopulations within a tissue that may be missed by traditional bulk RNA-sequencing methods. Although many existing methods have been developed to recognize known cell types, inferring novel cells may still be challenging in routine scRNA-seq analysis. Here we describe three lines of methods for inferring novel cells: unsupervised and outlier-detection-based methods, supervised and semi-supervised methods, and copy number variation (CNV)-based methods, as well as the corresponding situations that each method applies. We also provide implementation code and example usages to illustrate the available methods.

Inferring Tree-Shaped Single-Cell Trajectories with Totem.

Single-cell transcriptomics allows unbiased characterization of cell heterogeneity in a sample by profiling gene expression at single-cell level. These profiles capture snapshots of transient or steady states in dynamic processes, such as cell cycle, activation, or differentiation, which can be computationally ordered into a "flip-book" of cell development using trajectory inference methods. However, prediction of more complex topology structures, such as multifurcations or trees, remains challenging. In this chapter, we present two user-friendly protocols for inferring tree-shaped single-cell trajectories and pseudotime from single-cell transcriptomics data with Totem. Totem is a trajectory inference method that offers flexibility in inferring both nonlinear and linear trajectories and usability by avoiding the cumbersome fine-tuning of parameters. The QuickStart protocol provides a simple and practical example, whereas the GuidedStart protocol details the analysis step-by-step. Both protocols are demonstrated using a case study of human bone marrow CD34+ cells, allowing the study of the branching of three lineages: erythroid, lymphoid, and myeloid. All the analyses can be fully reproduced in Linux, macOS, and Windows operating systems (amd64 architecture) with >8 Gb of RAM using the provided docker image distributed with notebooks, scripts, and data in Docker Hub (elolab/repro-totem-ti). These materials are shared online under open-source license at https://elolab.github.io/Totem-protocol .

Single-cell RNA sequencing reveals special basal cells and fibroblasts in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is the most predominant type of idiopathic interstitial pneumonia and has an increasing incidence, poor prognosis, and unclear pathogenesis. In order to investigate the molecular mechanisms underlying IPF further, we performed single-cell RNA sequencing analysis on three healthy controls and five IPF lung tissue samples. The results revealed a significant shift in epithelial cells (ECs) phenotypes in IPF, which may be attributed to the differentiation of alveolar type 2 cells to basal cells. In addition, several previously unrecognized basal cell subtypes were preliminarily identified, including extracellular matrix basal cells, which were increased in the IPF group. We identified a special population of fibroblasts that highly expressed extracellular matrix-related genes, POSTN, CTHRC1, COL3A1, COL5A2, and COL12A1. We propose that the close interaction between ECs and fibroblasts through ligand-receptor pairs may have a critical function in IPF development. Collectively, these outcomes provide innovative perspectives on the complexity and diversity of basal cells and fibroblasts in IPF and contribute to the understanding of possible mechanisms in pathological lung fibrosis.

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations.

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.

Single Cell RNAseq Analysis of Cytokine-Treated Human Islets: Association of Cellular Stress with Impaired Cytokine Responsiveness.

Pancreatic ß-cells are essential for survival, being the only cell type capable of insulin secretion. While they are believed to be vulnerable to damage by inflammatory cytokines such as interleukin-1 beta (IL-1ß) and interferon-gamma, we have recently identified physiological roles for cytokine signaling in rodent ß-cells that include the stimulation of antiviral and antimicrobial gene expression and the inhibition of viral replication. In this study, we examine cytokine-stimulated changes in gene expression in human islets using single-cell RNA sequencing. Surprisingly, the global responses of human islets to cytokine exposure were remarkably blunted compared to our previous observations in the mouse. The small population of human islet cells that were cytokine responsive exhibited increased expression of IL-1ß-stimulated antiviral guanylate-binding proteins, just like in the mouse. Most human islet cells were not responsive to cytokines, and this lack of responsiveness was associated with high expression of genes encoding ribosomal proteins. We further correlated the expression levels of RPL5 with stress response genes, and when expressed at high levels, RPL5 is predictive of failure to respond to cytokines in all endocrine cells. We postulate that donor causes of death and isolation methodologies may contribute to stress of the islet preparation. Our findings indicate that activation of stress responses in human islets limits cytokine-stimulated gene expression, and we urge caution in the evaluation of studies that have examined cytokine-stimulated gene expression in human islets without evaluation of stress-related gene expression.

Coding and Non-Coding Transcriptomic Landscape of Aortic Complications in Marfan Syndrome.

Marfan syndrome (MFS) is a rare congenital disorder of the connective tissue, leading to thoracic aortic aneurysms (TAA) and dissection, among other complications. Currently, the most efficient strategy to prevent life-threatening dissection is preventive surgery. Periodic imaging applying complex techniques is required to monitor TAA progression and to guide the timing of surgical intervention. Thus, there is an acute demand for non-invasive biomarkers for diagnosis and prognosis, as well as for innovative therapeutic targets of MFS. Unraveling the intricate pathomolecular mechanisms underlying the syndrome is vital to address these needs. High-throughput platforms are particularly well-suited for this purpose, as they enable the integration of different datasets, such as transcriptomic and epigenetic profiles. In this narrative review, we summarize relevant studies investigating changes in both the coding and non-coding transcriptome and epigenome in MFS-induced TAA. The collective findings highlight the implicated pathways, such as TGF-ß signaling, extracellular matrix structure, inflammation, and mitochondrial dysfunction. Potential candidates as biomarkers, such as miR-200c, as well as therapeutic targets emerged, like Tfam, associated with mitochondrial respiration, or miR-632, stimulating endothelial-to-mesenchymal transition. While these discoveries are promising, rigorous and extensive validation in large patient cohorts is indispensable to confirm their clinical relevance and therapeutic potential.

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

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

SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer.

Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.