Analysis Pipeline Research Articles

Compositional data analysis enables statistical rigor in comparative glycomics

Comparative glycomics data are compositional data, where measured glycans are parts of a whole, indicated by relative abundances. Applying traditional statistical analyses to these data often results in misleading conclusions, such as spurious “decreases” of glycans when other structures increase in abundance, or high false-positive rates for differential abundance. Our work introduces a compositional data analysis framework, tailored to comparative glycomics, to account for these data dependencies. We employ center log-ratio and additive log-ratio transformations, augmented with a scale uncertainty/information model, to introduce a statistically robust and sensitive data analysis pipeline. Applied to comparative glycomics datasets, including known glycan concentrations in defined mixtures, this approach controls false-positive rates and results in reproducible biological findings. Additionally, we present specialized analysis modalities: alpha- and beta-diversity analyze glycan distributions within and between samples, while cross-class glycan correlations shed light on previously undetected interdependencies. These approaches reveal insights into glycome variations that are critical to understanding roles of glycans in health and disease.

Separating deterministic and stochastic gravitational wave signals in realistic pulsar timing array datasets

Recent observations by pulsar timing arrays (PTAs) suggest the presence of gravitational wave (GW) signals that potentially originate from supermassive black hole binaries (SMBHBs). These binaries can generate two kinds of signals: a stochastic gravitational wave background (GWB) or a deterministic continuous gravitational wave (CGW). The ability to correctly recognize and separate them is crucial for accurate signal recovery and astrophysical interpretation. This paper is aimed at investigating the interaction between stochastic GWB and deterministic CGW signals with the analysis pipelines currently available. We focus on understanding potential misinterpretations and biases in the parameter estimation when these signals are analysed separately or altogether. To this end, we performed several realistic simulations based on the European PTA 24.8yr dataset. We first injected either a GWB or a CGW into five datasets (of three GWB realisations and two CGW realisations) with identical noise. We analysed each signal type independently and then we analysed data sets containing both a stochastic GWB and a single resolvable CGW. We compared the parameter estimations using different search models, including Earth term (ET) only or combined Earth and pulsar term (ET+PT) CGW templates, along with correlated or uncorrelated power law GWB templates. We show that when searched for independently, the GWB and CGW signals can be misinterpreted (i.e. they can be confused with each other) and only a combined search is able to recover the true signal present. For datasets containing both a GWB and a CGW, failure to account for the latter biases the recovery of the GWB; however, when we perform a combined search, both GWB and CGW parameters can be recovered without any strong bias. Care must be taken with the method used to perform combined searches on these multi-component datasets, as the CGW PT can be misinterpreted as a common uncorrelated red noise. However, this can be avoided by conducting direct searches for a correlated GWB plus a CGW (ET+PT). Our study underscores the importance of combined searches to ensure unbiased recovery of GWB parameters in the presence of strong CGWs. This is crucial to accurately interpreting the signal recently found in PTA data and it is a first step towards a robust framework for disentangling stochastic and deterministic GW components in more sensitive datasets in the future.

Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach

BackgroundMatrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF MS) is proposed for mosquito species identification. The absence of public repositories sharing mass spectra and open-source data analysis pipelines for fingerprint matching to mosquito species limits the widespread use of this technology. The objective of this study was to develop a free open-source data analysis pipeline for Anopheles species identification with MALDI–TOF MS.MethodsAnopheles mosquitoes were captured in 33 villages in Karen (Kayin) state in Myanmar. A subset of 403 specimens was selected for inclusion in either the reference or the test panel (270 and 133 specimens, respectively). Three hundred fifty-nine specimens could be identified with DNA barcodes and were assigned to 21 sensu stricto species and five sibling species pairs or complexes. A total of 3584 mass spectra of the head of these specimens identified with DNA barcoding were acquired and the similarity between mass spectra was quantified using a cross-correlation approach adapted from the published literature. A reference mass spectra database was created using all spectra of the PCR-identified specimens assigned to the reference panel. A simulation experiment was carried out by querying the reference database with the spectra of the test panel to evaluate the performance of species identification with MALDI–TOF MS at varying thresholds of the cross-correlation index for the algorithm to output an identification result and with varying numbers of technical replicates for the tested specimens, considering PCR identification results as the reference.ResultsWith one spot and a threshold value of −14 for the cross-correlation index on the log scale, the sensitivity was 0.99 [95% credible interval (CrI): 0.98–1.00], the predictive positive value was 0.99 (95% CrI: 0.98–0.99), and the accuracy was 0.98 (95% CrI: 0.97–0.99). It was not possible to directly estimate the sensitivity and negative predictive value because there was no true negative (i.e., queries of species not referenced in the database) in the assessment.ConclusionsThe cross-correlation approach can be used to match mass spectral fingerprints to predefined taxa. MALDI–TOF MS is a valuable tool for rapid, accurate, and affordable identification of Anopheles species.Graphical abstract

MpactR: an R adaptation of the metabolomics peak analysis computational tool (MPACT) for use in reproducible data analysis pipelines.

mpactR automates pre-processing of liquid chromatography-tandem mass spectrometry (LC-MS/MS) data from microbiological samples to correct mispicked peaks, resolve inter-sample variation in abundance across technical replicates, account for in-source ion fragmentation, and remove background noise to yield high-quality mass spectrometry features. The package is available through CRAN and GitHub.

Effect of Postoperative Neck-Shaft and Anteversion Angles on Biomechanical Outcomes in Proximal Femoral Osteotomy: An In Silico Study.

Effective surgical planning is crucial for maximizing patient outcomes following complex orthopedic procedures such as proximal femoral osteotomy. In silico simulations can be used to assess how surgical variations in proximal femur geometry, such as femur neck-shaft and anteversion angles, affect postoperative system mechanics. This study investigated the sensitivity of femur mechanics to postoperative neck-shaft angles, anteversion angles, and osteotomy contact areas using patient-specific finite element analysis informed by neuromusculoskeletal models. A sequential neuromusculoskeletal modeling and finite element analysis pipeline was used to simulate postoperative mechanics in three pediatric patients with varying demographic and anatomic features. Nine surgical configurations derived from permutations of the clinical envelope of neck-shaft angles and anteversion angles were simulated for the stance phase of gait. The outcome mechanics assessed were peak von Mises stresses on the bone-implant contact surfaces as well as interfragmentary movement and strain on the osteotomy location. Peak von Mises stress and interfragmentary movement and strain were on average 38% more sensitive to surgical variation in neck-shaft angle compared to anteversion angle. A significant negative correlation was detected between contact area and interfragmentary movement (r = -0.90, p < 0.0001) and strain (r = -0.45, p = 0.017). Overall findings suggest neck-shaft angle significantly influences postoperative femur mechanics and highlight the importance of maximizing contact area to limit interfragmentary motion and foster an optimal mechanical environment for bone healing and callus formation following proximal femoral osteotomy. Between-patient variation in sensitivity to proximal femoral geometry reinforced the importance of patient-specific surgical planning.

MEPFeatX—automated feature extraction of motor-evoked potentials in transcranial magnetic stimulation

Motor evoked potentials (MEPs) are an important measure in transcranial magnetic stimulation (TMS) when assessing neuronal excitability in clinical diagnostics related to motor function, as well as in neuroscience research. However, manual feature extraction from large datasets can be time-consuming and prone to human error, and valuable features, such as MEP polyphasia and duration, are often neglected. Several packages have been developed to simplify the process; however, they are often tailored to specific studies or are not accessible. Here, we introduce MEPFeatX, a verified MATLAB package designed for automated and comprehensive MEP feature extraction across a wide range of stimulation paradigms. MEPFeatX is designed and documented for easy integration into any MEP analysis pipeline. Primed templates for specific paradigms, as well as additional analysis coded in R language, are also provided. Thus, MEPFeatX provides its users with a comprehensive and accurate set of MEP features, along with their visuals, facilitating quick and reliable MEP analysis in TMS studies.

Towards constructing a generalized structural 3D breathing human lung model based on experimental volumes, pressures, and strains.

Respiratory diseases represent a significant healthcare burden, as evidenced by the devastating impact of COVID-19. Biophysical models offer the possibility to anticipate system behavior and provide insights into physiological functions, advancements which are comparatively and notably nascent when it comes to pulmonary mechanics research. In this context, an Inverse Finite Element Analysis (IFEA) pipeline is developed to construct the first continuously ventilated three-dimensional structurally representative pulmonary model informed by both organ- and tissue-level breathing experiments from a cadaveric human lung. Here we construct a generalizable computational framework directly validated by pressure, volume, and strain measurements using a novel inflating apparatus interfaced with adapted, lung-specific, digital image correlation techniques. The parenchyma, pleura, and airways are represented with a poroelastic formulation to simulate pressure flows within the lung lobes, calibrating the model's material properties with the global pressure-volume response and local tissue deformations strains. The optimization yielded the following shear moduli: parenchyma (2.8 kPa), airways (0.2 kPa), and pleura (1.7 Pa). The proposed complex multi-material model with multi-experimental inputs was successfully developed using human lung data, and reproduced the shape of the inflating pressure-volume curve and strain distribution values associated with pulmonary deformation. This advancement marks a significant step towards creating a generalizable human lung model for broad applications across animal models, such as porcine, mouse, and rat lungs to reproduce pathological states and improve performance investigations regarding medical therapeutics and intervention.

Reanalysis of Exome Sequencing Data in the Indian Undiagnosed Diseases Program: Improving Diagnostic Yield and Ending Diagnostic Odyssey.

In 2021, the Indian Undiagnosed Diseases Program was initiated for patients without a definite diagnosis despite extensive evaluation in four participating sites. Between February 2021 and March 2023, a total of 88 patients were recruited and underwent deep phenotyping. A uniform methodology for data re-analysis was implemented as the first step prior to conducting additional genomic testing. The largest cohort was of 38 patients with neurodevelopmental disorders (NDD). A genetic diagnosis was achieved in 24 of the 88 patients (27.2%), including 7 cases within the NDD cohort. Factors contributing to the increased diagnostic yield included: (a) identification of a novel disease association in DAAM2 gene, and (b) limitations of the standard analysis pipeline, particularly for synonymous variants in SELENOI and KIAA0753 genes, non-frameshift variant in GLRX5 gene, low-coverage variant in GJC2 gene, large deletions in PCNT and PHKG2 genes, and intronic variants in VPS33B and FBN1. Improved phenotyping led to a diagnosis in three cases, while genomic variants missed in the previous bioinformatics analysis were identified in 12 cases. The study also contributed to the development of enhanced bioinformatics scripts for variant prioritization and more refined literature search for novel disease associations. It highlights the importance of incorporating data reanalysis into clinical workflows before pursuing advanced diagnostic tests, particularly in resource-limited settings where healthcare expenses are often borne out of pocket.

HoloFood Data Portal: holo-omic datasets for analysing host-microbiota interactions in animal production.

The HoloFood project used a hologenomic approach to understand the impact of host-microbiota interactions on salmon and chicken production by analysing multiomic data, phenotypic characteristics, and associated metadata in response to novel feeds. The project's raw data, derived analyses, and metadata are deposited in public, open archives (BioSamples, European Nucleotide Archive, MetaboLights, and MGnify), so making use of these diverse data types may require access to multiple resources. This is especially complex where analysis pipelines produce derived outputs such as functional profiles or genome catalogues. The HoloFood Data Portal is a web resource that simplifies access to the project datasets. For example, users can conveniently access multiomic datasets derived from the same individual or retrieve host phenotypic data with a linked gut microbiome sample. Project-specific metagenome-assembled genome and viral catalogues are also provided, linking to broader datasets in MGnify. The portal stores only data necessary to provide these relationships, with possible linking to the underlying repositories. The portal showcases a model approach for how future multiomics datasets can be made available. Database URL: https://www.holofooddata.org.

Optimized molecule detection in localization microscopy with selected false positive probability

Single-molecule localization microscopy (SMLM) allows imaging beyond the diffraction limit. Detection of molecules is a crucial initial step in SMLM. False positive detections, which are not quantitatively controlled in current methods, are a source of artifacts that affect the entire SMLM analysis pipeline. Furthermore, current methods lack standardization, which hinders reproducibility. Here, we present an optimized molecule detection method which combines probabilistic thresholding with theoretically optimal filtering. The probabilistic thresholding enables control over false positive detections while optimal filtering minimizes false negatives. A theoretically optimal Poisson matched filter is used as a performance benchmark to evaluate existing filtering methods. Overall, our approach allows the detection of molecules in a robust, single-parameter and user-unbiased manner. This will minimize artifacts and enable data reproducibility in SMLM.

Insect-specific RNA viruses detection in Field-Caught Aedes aegypti mosquitoes from Argentina using NGS technology.

Mosquitoes are the primary vectors of arthropod-borne pathogens. Aedes aegypti is one of the most widespread mosquito species worldwide, responsible for transmitting diseases such as Dengue, Zika, and Chikungunya, among other medically significant viruses. Characterizing the array of viruses circulating in mosquitoes, particularly in Aedes aegypti, is a crucial tool for detecting and developing novel strategies to prevent arbovirus outbreaks. In this study, we address the implementation of a sequencing and analysis pipeline based on the Oxford Nanopore Technologies MinION Mk1b system, for arboviral detection in field-caught mosquitoes from Argentina. Full genome of Humaita Tubiacanga Virus (HTV), Phasi Charoen-like Phasivirus (PCLV), Aedes aegypti totivirus (AaeTV) has been sequenced in three distinct regions of Argentina comprising Buenos Aires province, Santa Fe province and the northern province of Salta. Viral sequences enriched by SISPA and coupled with Nanopore sequencing can be a useful tool for viral surveillance, not only for detecting viruses that have a high impact on human and animal health, but also for detecting insect-specific viruses that could promote the transmission of arboviruses.

Chronology of transcriptome and proteome expression during early Arabidopsis flower development.

The complex gene regulatory landscape underlying early flower development in Arabidopsis has been extensively studied through transcriptome profiling, and gene networks controlling floral organ development have been derived from the analyses of genome wide binding of key transcription factors. In contrast, the dynamic nature of the proteome during the flower development process is much less understood. In this study, we characterized the floral proteome at different stages during early flower development and correlated it with unbiased transcript expression data. Shotgun proteomics and transcript profiling were conducted using an APETALA1-based floral induction system. A specific analysis pipeline to process the time-course proteomics data was developed. In total, 8,924 proteins and 23,069 transcripts were identified. Co-expression analysis revealed that RNA-protein pairs clustered in various expression pattern modules. An overall positive correlation between RNA and protein level changes was observed, but subgroups of RNA/protein pairs with anticorrelated gene expression changes were also identified and found to be enriched in hormone responsive pathways. In addition, the RNA-seq dataset reported here further expanded the identification of genes whose expression changes during early flower development, and its combination with previously published AP1 ChIP-seq datasets allowed the identification of additional AP1 direct and high-confidence targets.

Spatiotemporal dynamics of fetal liver hematopoietic niches.

Embryonic hematopoietic cells develop in the fetal liver (FL), surrounded by diverse non-hematopoietic stromal cells. However, the spatial organization and cytokine production patterns of the stroma during FL development remain poorly understood. Here, we characterized and mapped the hematopoietic and stromal cell populations at early (E12.5-14.5) FL stages, revealing that while hepatoblasts were the primary source of hematopoietic growth factors, other stromal cells-including mesenchymal, mesothelial, and endothelial cells-also contributed to this signaling network. Using a dedicated image analysis pipeline, we quantified cell distances to tissue structures and defined neighbor relationships, uncovering that different hematopoietic progenitors exhibit distinct preferences for neighboring stromal cells and show developmental changes in spatial distribution. Notably, our data suggest that the sub-mesothelium region plays a prominent role in early fetal hematopoiesis. This approach offers a valuable tool for studying complex cellular interactions in biological systems, providing new insights into hematopoietic niche organization during development.

The spatial organization of cDC1 with CD8+ T cells is critical for the response to immune checkpoint inhibitors in melanoma patients.

Dendritic cells (DCs) are promising targets for cancer immunotherapies because of their central role in the initiation and control of immune responses. The rare cDC1 population is of particular interest because of its remarkable ability to cross-present antigens (Ag) to CD8+ T cells, to promote Th1 cell polarization and NK cell activation and recruitment. However, the spatial organization and specific functions of cDC1s in response to immunotherapy remain to be clearly characterized in human tumors. Here, we implemented a multiplexed immunofluorescence analysis pipeline coupled with computational image analysis to determine the spatial organization of the cDC1 subset in a cohort of skin lesions from advanced melanoma patients treated with immune checkpoint inhibitors (ICI). For this, we performed a whole-slide image analysis of cDC1 infiltration and distribution as well as their spatial interactions with key immune partners such as CD8+ T cells and pDC according to the response of patients to ICI. We also analyzed LAMP3+-DC, which correspond to a mature subset of tumor-infiltrating DCs. Distance and cell network analyses demonstrated that cDC1s exhibited a scattered distribution compared to tumor-infiltrating pDCs and LAMP3+-DCs, which were preferentially organized in dense areas with high homotypic connections. Interestingly, the proximity and interactions between CD8+ T cells and cDC1s were positively associated with the response to ICI. In conclusion, our study unravels the complex spatial organization of cDC1s and their interactions with CD8+ T cells in melanoma patient lesions, shedding light on their pivotal role in shaping the response to ICI.

Conjunctival Bulbar Redness Extraction Pipeline for High-Resolution Ocular Surface Photography.

To extract conjunctival bulbar redness from standardized high-resolution ocular surface photographs of a novel imaging system by implementing an image analysis pipeline. Data from two trials (healthy; outgoing ophthalmic clinic) were collected, processed, and used to train a machine learning model for ocular surface segmentation. Various regions of interest were defined to globally and locally extract a redness biomarker based on color intensity. The image-based redness scores were correlated to clinical gradings (Efron) for validation. The model to determine the regions of interest was verified for a segmentation performance, yielding mean intersections over union of 0.9639 (iris) and 0.9731 (ocular surface). All trial data were analyzed and a digital grading scale for the novel imaging system was established. Photographs and redness scores from visits weeks apart showed good feasibility and reproducibility. For scores within the same session, a mean coefficient of variation of 4.09% was observed. A moderate positive Spearman correlation (0.599) was found with clinical grading. The proposed conjunctival bulbar redness extraction pipeline demonstrates that by using standardized imaging, a segmentation model and image-based redness scores' external eye photography can be classified and evaluated. Therefore, it shows the potential to provide eye care professionals with an objective tool to grade ocular redness and facilitate clinical decision-making in a high-throughput manner. To empower clinicians and researchers with a high-throughput workflow by standardized imaging combined with an analysis tool based on artificial intelligence to objectively determine an image-based redness score.

CWL-Based Analysis Pipeline for Hi-C Data: From FASTQ Files to Matrices.

Over a decade has passed since the development of the Hi-C method for genome-wide analysis of 3D genome organization. Hi-C utilizes next-generation sequencing (NGS) technology to generate large-scale chromatin interaction data, which has accumulated across a diverse range of species and cell types, particularly in eukaryotes. There is thus a growing need to streamline the process of Hi-C data analysis to utilize these data sets effectively. Hi-C generates data that are much larger compared to other NGS techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) or RNA-seq, making the data reanalysis process computationally expensive. In an effort to bridge this resource gap, the 4D Nucleome (4DN) Data Portal has reanalyzed approximately 600 Hi-C data sets, allowing users to access and utilize the analyzed data. In this chapter, we provide detailed instructions for the implementation of the common workflow language (CWL)-based Hi-C analysis pipeline adopted by the 4DN Data Portal ecosystem. This reproducible and portable pipeline generates standard Hi-C contact matrices in formats such as .hic or .mcool from FASTQ files. It enables users to output their own Hi-C data in the same format as those registered in the 4DN Data portal, facilitating comparative analysis using data registered in the portal. Our custom-made scripts are available on GitHub at https://github.com/kuzobuta/4dn_cwl_pipeline .

Testing the robustness of the BAO determination in the presence of massive neutrinos

We study the robustness of the Baryon Acoustic Oscillation (BAO) feature in the large-scale structure in the presence of massive neutrinos. In the standard BAO analysis pipeline a reference cosmological model is assumed to boost the BAO peak through the so-called reconstruction technique and in the modelling of the BAO feature to extract the cosmological information. State-of-the art spectroscopic BAO measurements, such as the Dark Energy Spectroscopic Instrument claim an aggregate precision of 0.52% on the BAO scale, with a systematic error of 0.1% associated to the assumption of a reference cosmology when measuring and analyzing the BAO feature. While the systematic effect induced by this arbitrary choice of fiducial cosmology has been studied for a wide range of ΛCDM-like models, it has not yet been tested for reference cosmologies with massive neutrinos with the precision afforded by next generation surveys. In this context, we employ the Quijote high-resolution dark-matter simulations with haloes above a mass of M ∼ 2×1013 h -1 M ⊙, with different values for the total sum of neutrinos masses, ∑mν [eV] = 0, 0.1, 0,2, 0.4 to study and quantify the impact of the pipeline's built-in assumption of massless neutrinos on the measurement of the BAO signal, with a special focus on the BAO reconstruction technique. We determine that any additional systematic bias introduced by the assumption of massless neutrinos is no greater than 0.1% (0.2%) for the isotropic (anisotropic) measurement. We expect these conclusions also hold for galaxies provided that neutrino properties do not alter the galaxy-halo connection.

Identifying Privacy Personas

Privacy personas capture the differences in user segments with respect to one’s knowledge, behavioural patterns, level of self-efficacy, and perception of the importance of privacy protection. Modelling these differences is essential for appropriately choosing personalised communication about privacy (e.g. to increase literacy) and for defining suitable choices for privacy enhancing technologies (PETs). While various privacy personas have been derived in the literature, they group together people who differ from each other in terms of important attributes such as perceived or desired level of control, and motivation to use PET. To address this lack of granularity and comprehensiveness in describing personas, we propose eight personas that we derive by combining qualitative and quantitative analysis of the responses to an interactive educational questionnaire. We design an analysis pipeline that uses divisive hierarchical clustering and Boschloo’s statistical test of homogeneity of proportions to ensure that the elicited clusters differ from each other based on a statistical measure. Additionally, we propose a new measure for calculating distances between questionnaire responses, that accounts for the type of the question (closed- vs open-ended) used to derive traits. We show that the proposed privacy personas statistically differ from each other. We statistically validate the proposed personas and also compare them with personas in the literature, showing that they provide a more granular and comprehensive understanding of user segments, which will allow to better assist users with their privacy needs.

Highly-Multiplexed Immunofluorescence PhenoCycler Panel for Murine FFPE Yields Insight into Tumor Microenvironment Immunoengineering

Spatial proteomics profiling is an emerging set of technologies that has the potential to elucidate the cell types, interactions, and molecular signatures that make up complex tissue microenvironments, with applications in the study of cancer, immunity, and much more. An emerging technique in the field is Co-Detection-by-indEXing (CODEX), recently renamed as the PhenoCycler system. This is a highly-multiplexed immunofluorescence imaging technology that relies on oligonucleotide-barcoded antibodies and cyclic immunofluorescence to visualize many antibody markers in a single specimen while preserving tissue architecture. Existing PhenoCycler panels are primarily designed for fresh-frozen tissues. Formalin-fixed paraffin-embedded (FFPE) blocks offer several advantages in preclinical research, but few antibody clones have been identified in this setting for PhenoCycler imaging. Here, we present a novel PhenoCycler panel of 28 validated antibodies for murine FFPE tissues. We describe our workflow for selecting and validating clones, barcoding antibodies, designing our panel, and performing multiplex imaging. We further detail our analysis pipeline for comparing marker expressions, clustering and phenotyping single-cell proteomics data, and quantifying spatial relationships. We then apply our panel and analysis protocol to profile the effects of three gene-delivery nanoparticle formulations, in combination with systemic anti-PD1, on the murine melanoma tumor immune microenvironment. Intralesional delivery of genes expressing the costimulatory molecule 4-1BBL and the cytokine IL-12 led to a shift towards intratumoral M1 macrophage polarization and promoted closer associations between intratumoral CD8 T cells and macrophages. Delivery of IFNγ, in addition to 4-1BBL and IL-12, further increased markers of antigen presentation on tumor cells and intratumoral antigen-presenting cells but also promoted greater expression of checkpoint marker PD-L1 and closer associations between intratumoral CD8 T cells and PD-L1-expressing tumor cells. These findings help to explain the benefits of 4-1BBL and IL-12 delivery while offering additional mechanistic insights into the limitations of IFNγ therapeutic efficacy.

Methods for Visualizing and Quantifying Cholesterol Distribution in Mammalian Cells Using Filipin and D4 Probes.

Cholesterol is a key component of biological membranes and, like many cellular lipids, is unevenly distributed among organelles. Disruptions in cholesterol trafficking are associated with various pathologies, including lysosomal lipid storage disorders, often characterized by intracellular cholesterol accumulation. A significant challenge in studying cholesterol trafficking is the lack of easy methods to trace this molecule in situ. Fluorescent probes that specifically bind cholesterol have enabled the visualization and imaging of cholesterol distribution within cells. This chapter details optimized methods for visualizing and quantifying free cholesterol at the plasma membrane and intracellulaly, both inindividual cellsand in large cell populations. These methods use two fluorescent probes: the D4 fragment of perfringolysin O fused to monomeric EGFP (mEGFP-D4 and the more sensitive mutant mEGFP-D4H) and the polyene macrolide filipin. We describe robust methods for quantifying plasma membrane cholesterol by flow cytometry and to visualize intracellular cholesterol poolsby light microscopy. Furthermore, we introduce a refined filipin staining protocol that enhances intracellular cholesterol detection. For precise quantification, we developed an automated image analysis pipeline. This chapter provides a comprehensive guide for staining and quantifying cellular cholesterol, offering valuable tools for studying cholesterol dynamics in mammalian cells.