Mitochondrial dysfunction and increased reactive oxygen species production in MECP2 mutant astrocytes and their impact on neurons.

Studies on MECP2 function and its implications in Rett Syndrome (RTT) have traditionally centered on neurons. Here, using human embryonic stem cell (hESC) lines, we modeled MECP2 loss-of-function to explore its effects on astrocyte (AST) development and dysfunction in the brain. Ultrastructural analysis of RTT hESC-derived cerebral organoids revealed significantly smaller mitochondria compared to controls (CTRs), particularly pronounced in glia versus neurons. Employing a multiomics approach, we observed increased gene expression and accessibility of a subset of nuclear-encoded mitochondrial genes upon mutation of MECP2 in ASTs compared to neurons. Analysis of hESC-derived ASTs showed reduced mitochondrial respiration and altered key proteins in the tricarboxylic acid cycle and electron transport chain in RTT versus CTRs. Additionally, RTT ASTs exhibited increased cytosolic amino acids under basal conditions, which were depleted upon increased energy demands. Notably, mitochondria isolated from RTT ASTs exhibited increased reactive oxygen species and influenced neuronal activity when transferred to cortical neurons. These findings underscore MECP2 mutation's differential impact on mitochondrial and metabolic pathways in ASTs versus neurons, suggesting that dysfunctional AST mitochondria may contribute to RTT pathophysiology by affecting neuronal health.

Discovery of therapeutic targets in cancer using chromatin accessibility and transcriptomic data.

Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options.

Review and Evaluate the Bioinformatics Analysis Strategies of ATAC-seq and CUT&Tag Data.

Efficient and reliable profiling methods are essential to study epigenetics. Tn5, one of the first identified prokaryotic transposases with high DNA-binding and tagmentation efficiency, is widely adopted in different genomic and epigenomic protocols for high-throughputly exploring the genome and epigenome. Based on Tn5, the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and the Cleavage Under Targets and Tagmentation (CUT&Tag) were developed to measure chromatin accessibility and detect DNA-protein interactions. These methodologies can be applied to large amounts of biological samples with low-input levels, such as rare tissues, embryos, and sorted single cells. However, fast and proper processing of these epigenomic data has become a bottleneck because massive data production continues to increase quickly. Furthermore, inappropriate data analysis can generate biased or misleading conclusions. Therefore, it is essential to evaluate the performance of Tn5-based ATAC-seq and CUT&Tag data processing bioinformatics tools, many of which were developed mostly for analyzing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Here, we conducted a comprehensive benchmarking analysis to evaluate the performance of eight popular software for processing ATAC-seq and CUT&Tag data. We compared the sensitivity, specificity, and peak width distribution for both narrow-type and broad-type peak calling. We also tested the influence of the availability of control IgG input in CUT&Tag data analysis. Finally, we evaluated the differential analysis strategies commonly used for analyzing the CUT&Tag data. Our study provided comprehensive guidance for selecting bioinformatics tools and recommended analysis strategies, which were implemented into Docker/Singularity images for streamlined data analysis.

Mir221/222 drive synovial hyperplasia and arthritis by targeting cell cycle inhibitors and chromatin remodeling components.

miRNAs constitute fine-tuners of gene expression and are implicated in a variety of diseases spanning from inflammation to cancer. miRNA expression is deregulated in rheumatoid arthritis (RA); however, their specific role in key arthritogenic cells such as the synovial fibroblast (SF) remains elusive. Previous studies have shown that Mir221/222 expression is upregulated in RA SFs. Here, we demonstrate that TNF and IL-1ß but not IFN-γ activated Mir221/222 gene expression in murine SFs. SF-specific overexpression of Mir221/222 in huTNFtg mice led to further expansion of SFs and disease exacerbation, while its total ablation led to reduced SF expansion and attenuated disease. Mir221/222 overexpression altered the SF transcriptional profile igniting pathways involved in cell cycle and ECM (extracellular matrix) regulation. Validation of targets of Mir221/222 revealed cell cycle inhibitors Cdkn1b and Cdkn1c, as well as the epigenetic regulator Smarca1. Single-cell ATAC-seq data analysis revealed increased Mir221/222 gene activity in pathogenic SF subclusters and transcriptional regulation by Rela, Relb, Junb, Bach1, and Nfe2l2. Our results establish an SF-specific pathogenic role of Mir221/222 in arthritis and suggest that its therapeutic targeting in specific subpopulations could lead to novel fibroblast-targeted therapies.

Muscle growth differences in Lijiang pigs revealed by ATAC-seq multi-omics.

As one of the largest tissues in the animal body, skeletal muscle plays a pivotal role in the production and quality of pork. Consequently, it is of paramount importance to investigate the growth and developmental processes of skeletal muscle. Lijiang pigs, which naturally have two subtypes, fast-growing and slow-growing, provide an ideal model for such studies by eliminating breed-related influences. In this study, we selected three fast-growing and three slow-growing 6-month-old Lijiang pigs as subjects. We utilized assay for transposase-accessible chromatin with sequencing (ATAC-seq) combined with genomics, RNA sequencing, and proteomics to screen for differentially expressed genes and transcription factors linked to increased longissimus dorsi muscle volume in Lijiang pigs. We identified 126 genes through ATAC-seq, including PPARA, TNRC6B, NEDD1, and FKBP5, that exhibited differential expression patterns during muscle growth. Additionally, we identified 59 transcription factors, including Foxh1, JunB, Mef2 family members (Mef2a/b/c/d), NeuroD1, and TEAD4. By examining open chromatin regions (OCRs) with significant genetic differentiation, genes such as SAV1, CACNA1H, PRKCG, and FGFR4 were found. Integrating ATAC-seq with transcriptomics and transcriptomics with proteomics, we identified differences in open chromatin regions, transcription, and protein levels of FKBP5 and SCARB2 genes in fast-growing and slow-growing Lijiang pigs. Utilizing multi-omics analysis with R packages, we jointed ATAC-seq, transcriptome, and proteome datasets, identifying enriched pathways related to glycogen metabolism and skeletal muscle cell differentiation. We pinpointed genes such as MYF6 and HABP2 that exhibit strong correlations across these diverse data types. This study provides a multi-faceted understanding of the molecular mechanisms that lead to differences in pig muscle fiber growth.

HOXD12 defines an age-related aggressive subtype of oligodendroglioma.

Oligodendroglioma, IDH-mutant and 1p/19q-codeleted has highly variable outcomes that are strongly influenced by patient age. The distribution of oligodendroglioma age is non-Gaussian and reportedly bimodal, which motivated our investigation of age-associated molecular alterations that may drive poorer outcomes. We found that elevated HOXD12 expression was associated with both older patient age and shorter survival in the TCGA (FDR < 0.01, FDR = 1e-5) and the CGGA (p = 0.03, p < 1e-3). HOXD12 gene body hypermethylation was associated with older age, higher WHO grade, and shorter survival in the TCGA (p < 1e-6, p < 0.001, p < 1e-3) and with older age and higher WHO grade in Capper et al. (p < 0.002, p = 0.014). In the TCGA, HOXD12 gene body hypermethylation and elevated expression were independently prognostic of NOTCH1 and PIK3CA mutations, loss of 15q, MYC activation, and standard histopathological features. Single-nucleus RNA and ATAC sequencing data showed that HOXD12 activity was elevated in neoplastic tissue, particularly within cycling and OPC-like cells, and was associated with a stem-like phenotype. A pan-HOX DNA methylation analysis revealed an age and survival-associated HOX-high signature that was tightly associated with HOXD12 gene body methylation. Overall, HOXD12 expression and gene body hypermethylation were associated with an older, atypically aggressive subtype of oligodendroglioma.

A molecular pathway for cancer cachexia-induced muscle atrophy revealed at single-nucleus resolution.

Cancer cachexia is a prevalent and often fatal wasting condition that cannot be fully reversed with nutritional interventions. Muscle atrophy is a central component of the syndrome, but the mechanisms whereby cancer leads to skeletal muscle atrophy are not well understood. We performed single-nucleus multi-omics on skeletal muscles from a mouse model of cancer cachexia and profiled the molecular changes in cachexic muscle. Our results revealed the activation of a denervation-dependent gene program that upregulates the transcription factor myogenin. Further studies showed that a myogenin-myostatin pathway promotes muscle atrophy in response to cancer cachexia. Short hairpin RNA inhibition of myogenin or inhibition of myostatin through overexpression of its endogenous inhibitor follistatin prevented cancer cachexia-induced muscle atrophy in mice. Our findings uncover a molecular basis of muscle atrophy associated with cancer cachexia and highlight potential therapeutic targets for this disorder.

Comparative single-cell analyses identify shared and divergent features of human and mouse kidney development.

The mammalian kidney maintains fluid homeostasis through diverse epithelial cell types generated from nephron and ureteric progenitor cells. To extend a developmental understanding of the kidney's epithelial networks, we compared chromatin organization (single-nuclear assay for transposase-accessible chromatin sequencing [ATAC-seq]; 112,864 nuclei) and gene expression (single-cell/nuclear RNA sequencing [RNA-seq]; 109,477 cells/nuclei) in the developing human (10.6-17.6 weeks; n = 10) and mouse (post-natal day [P]0; n = 10) kidney, supplementing analysis with published mouse datasets from earlier stages. Single-cell/nuclear datasets were analyzed at a species level, and then nephron and ureteric cellular lineages were extracted and integrated into a common, cross-species, multimodal dataset. Comparative computational analyses identified conserved and divergent features of chromatin organization and linked gene activity, identifying species-specific and cell-type-specific regulatory programs. In situ validation of human-enriched gene activity points to human-specific signaling interactions in kidney development. Further, human-specific enhancer regions were linked to kidney diseases through genome-wide association studies (GWASs), highlighting the potential for clinical insight from developmental modeling.

Enhancer AAVs for targeting spinal motor neurons and descending motor pathways in rodents and macaque.

Experimental access to cell types within the mammalian spinal cord is severely limited by the availability of genetic tools. To enable access to lower motor neurons (LMNs) and LMN subtypes, which function to integrate information from the brain and control movement through direct innervation of effector muscles, we generated single cell multiome datasets from mouse and macaque spinal cords and discovered putative enhancers for each neuronal population. We cloned these enhancers into adeno-associated viral vectors (AAVs) driving a reporter fluorophore and functionally screened them in mouse. The most promising candidate enhancers were then extensively characterized using imaging and molecular techniques and further tested in rat and macaque to show conservation of LMN labeling. Additionally, we combined enhancer elements into a single vector to achieve simultaneous labeling of upper motor neurons (UMNs) and LMNs. This unprecedented LMN toolkit will enable future investigations of cell type function across species and potential therapeutic interventions for human neurodegenerative diseases.

Molecular basis of urostyle development in frogs: genes and gene regulation underlying an evolutionary novelty.

Evolutionary novelties entail the origin of morphologies that enable new functions. These features can arise through changes to gene function and regulation. One key novelty is the fused rod at the end of the vertebral column in anurans, the urostyle. This feature is composed of a coccyx and a hypochord, both of which ossify during metamorphosis. To elucidate the genetic basis of these features, we used laser capture microdissection of these tissues and did RNA-seq and ATAC-seq at three developmental stages in tadpoles of Xenopus tropicalis. RNA-seq reveals that the coccyx and hypochord have two different molecular signatures. Neuronal (TUBB3) and muscle markers (MYH3) are upregulated in coccygeal tissues, whereas T-box genes (TBXT, TBXT.2), corticosteroid stress hormones (CRCH.1) and matrix metallopeptidases (MMP1, MMP8 and MMP13) are upregulated in the hypochord. ATAC-seq reveals potential regulatory regions that are observed in proximity to candidate genes that regulate ossification identified from RNA-seq. Even though an ossifying hypochord is only present in anurans, this ossification between the vertebral column and the notochord resembles a congenital vertebral anomaly seen prenatally in humans caused by an ectopic expression of the TBXT/TBXT.2 gene. This work opens the way to functional studies that can elucidate anuran bauplan evolution.

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.

Integrating Multi-Omics Data to Identify Key Functional Variants Affecting Feed Efficiency in Large White Boars.

Optimizing feed efficiency through the feed conversion ratio (FCR) is paramount for economic viability and sustainability. In this study, we integrated RNA-seq, ATAC-seq, and genome-wide association study (GWAS) data to investigate key functional variants associated with feed efficiency in pigs. Identification of differentially expressed genes in the duodenal and muscle tissues of low- and high-FCR pigs revealed that pathways related to digestion of dietary carbohydrate are responsible for differences in feed efficiency between individuals. Differential open chromatin regions identified by ATAC-seq were linked to genes involved in glycolytic and fatty acid processes. GWAS identified 211 significant single-nucleotide polymorphisms associated with feed efficiency traits, with candidate genes PPP1R14C, TH, and CTSD. Integration of duodenal ATAC-seq data and GWAS data identified six key functional variants, particularly in the 1500985-1509676 region on chromosome 2. In those regions, CTSD was found to be highly expressed in the duodenal tissues of pigs with a high feed conversion ratio, suggesting its role as a potential target gene. Overall, the integration of multi-omics data provided insights into the genetic basis of feed efficiency, offering valuable information for breeding more efficient pig breeds.

aPEAch: Automated Pipeline for End-to-End Analysis of Epigenomic and Transcriptomic Data.

With the advent of next-generation sequencing (NGS), experimental techniques that capture the biological significance of DNA loci or RNA molecules have emerged as fundamental tools for studying the epigenome and transcriptional regulation on a genome-wide scale. The volume of the generated data and the underlying complexity regarding their analysis highlight the need for robust and easy-to-use computational analytic methods that can streamline the process and provide valuable biological insights. Our solution, aPEAch, is an automated pipeline that facilitates the end-to-end analysis of both DNA- and RNA-sequencing assays, including small RNA sequencing, from assessing the quality of the input sample files to answering meaningful biological questions by exploiting the rich information embedded in biological data. Our method is implemented in Python, based on a modular approach that enables users to choose the path and extent of the analysis and the representations of the results. The pipeline can process samples with single or multiple replicates in batches, allowing the ease of use and reproducibility of the analysis across all samples. aPEAch provides a variety of sample metrics such as quality control reports, fragment size distribution plots, and all intermediate output files, enabling the pipeline to be re-executed with different parameters or algorithms, along with the publication-ready visualization of the results. Furthermore, aPEAch seamlessly incorporates advanced unsupervised learning analyses by automating clustering optimization and visualization, thus providing invaluable insight into the underlying biological mechanisms.

Integrative multi-omics increase resolution of the sea urchin posterior gut gene regulatory network at single-cell level.

Drafting gene regulatory networks (GRNs) requires embryological knowledge pertaining to the cell type families, information on the regulatory genes, causal data from gene knockdown experiments and validations of the identified interactions by cis-regulatory analysis. We use multi-omics involving next-generation sequencing to obtain the necessary information for drafting the Strongylocentrotus purpuratus (Sp) posterior gut GRN. Here, we present an update to the GRN using: (1) a single-cell RNA-sequencing-derived cell atlas highlighting the 2 day-post-fertilization (dpf) sea urchin gastrula cell type families, as well as the genes expressed at the single-cell level; (2) a set of putative cis-regulatory modules and transcription factor-binding sites obtained from chromatin accessibility ATAC-seq data; and (3) interactions directionality obtained from differential bulk RNA sequencing following knockdown of the transcription factor Sp-Pdx1, a key regulator of gut patterning in sea urchins. Combining these datasets, we draft the GRN for the hindgut Sp-Pdx1-positive cells in the 2 dpf gastrula embryo. Overall, our data suggest the complex connectivity of the posterior gut GRN and increase the resolution of gene regulatory cascades operating within it.

Multi-scale signaling and tumor evolution in high-grade gliomas.

Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.

Chromatin accessibility and transcriptional landscape in PK-15 cells during early exposure to Aflatoxin B1.

Aflatoxin B1 (AFB1) not only causes significant losses in livestock production but also poses a serious threat to human health. It is the most carcinogenic among known chemicals. Pigs are more susceptible to AFB1 and experience a higher incidence. However, the molecular mechanism of the toxic effect of AFB1 remains unclear. In this study, we used assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq to uncover chromatin accessibility and gene expression dynamics in PK-15 cells during early exposure to AFB1. We observed that the toxic effects of AFB1 involve signaling pathways such as p53, PI3K-AKT, Hippo, MAPK, TLRs, apoptosis, autophagy, and cancer pathways. Basic leucine zipper (bZIP) transcription factors (TFs), including AP-1, Fos, JunB, and Fra2, play a crucial role in regulating the biological processes involved in AFB1 challenge. Several new TFs, such as BORIS, HNF1b, Atf1, and KNRNPH2, represent potential targets for the toxic mechanism of AFB1. In addition, it is crucial to focus on the concentration of intracellular zinc ions. These findings will contribute to a better understanding of the mechanisms underlying AFB1-induced nephrotoxicity and offer new molecular targets.

Light-responsive transcription factors VvHYH and VvGATA24 mediate wax terpenoid biosynthesis in Vitis vinifera.

The cuticular wax that covers the surfaces of plants is the first barrier against environmental stresses and increasingly accumulates with light exposure. However, the molecular basis of light-responsive wax biosynthesis remains elusive. In grape (Vitis vinifera), light exposure resulted in higher wax terpenoid content and lower decay and abscission rates than controls kept in darkness. Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and RNA-seq data were integrated to draw the chromatin accessibility and cis-elements regulatory map to identify the potential action sites. Terpenoid synthase 12 (VvTPS12) and 3-Hydroxy-3-methylglutaryl-CoA reductase 2 (VvHMGR2) were identified as grape wax biosynthesis targets, while VvHYH and VvGATA24 were identified as terpenoid biosynthesis activators, as more abundant wax crystals and higher wax terpenoid content were observed in transiently overexpressed grape berries and Nicotiana benthamiana leaves. The interaction between VvHYH and the open chromatin of VvTPS12 was confirmed qualitatively using a dual luciferase assay and quantitatively using surface plasma resonance, with an equilibrium dissociation constant of 2.81 nM identified via the latter approach. Molecular docking simulation implied the structural nature of this interaction, indicating that 24 amino acid residues of VvHYH, including Arg106A, could bind to the VvTPS12 G-box cis-element. VvGATA24 directly bound to the open chromatin of VvHMGR2, with an equilibrium dissociation constant of 8.59 nM. 12 amino acid residues of VvGATA24, including Pro218B, interacted with the VvHMGR2 GATA-box cis-element. Our work characterizes the mechanism underlying light-mediated wax terpenoid biosynthesis and provides gene targets for future molecular breeding.

Conservation of cis-Regulatory Syntax Underlying Deuterostome Gastrulation.

Throughout embryonic development, the shaping of the functional and morphological characteristics of embryos is orchestrated by an intricate interaction between transcription factors and cis-regulatory elements. In this study, we conducted a comprehensive analysis of deuterostome cis-regulatory landscapes during gastrulation, focusing on four paradigmatic species: the echinoderm Strongylocentrotus purpuratus, the cephalochordate Branchiostoma lanceolatum, the urochordate Ciona intestinalis, and the vertebrate Danio rerio. Our approach involved comparative computational analysis of ATAC-seq datasets to explore the genome-wide blueprint of conserved transcription factor binding motifs underlying gastrulation. We identified a core set of conserved DNA binding motifs associated with 62 known transcription factors, indicating the remarkable conservation of the gastrulation regulatory landscape across deuterostomes. Our findings offer valuable insights into the evolutionary molecular dynamics of embryonic development, shedding light on conserved regulatory subprograms and providing a comprehensive perspective on the conservation and divergence of gene regulation underlying the gastrulation process.

Measuring B Cell Chromatin Accessibility by One-Step ATAC-seq.

The Assay for Transposase Accessible Chromatin (ATAC)-seq protocol is optimized to generate global maps of accessible chromatin using limited cell inputs. The Tn5 transposase tagmentation reaction simultaneously fragments and tags the accessible DNA with Illumina Nextera sequencing adapters. Fragmented and adapter tagged DNA is then purified and PCR amplified with dual indexing primers to generate a size-specific sequencing library. The One-Step workflow below outlines the Tn5 nuclei transposition from a range of cell inputs followed by PCR amplification to generate a sequencing library.

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.