Inter- and intra-observer variation in phytolith morphometry.

BACKGROUND: Archaeobotanists and palaeoecologists extensively use geometric morphometrics to identify plant opal phytoliths. Particularly when applied to assemblages of phytoliths from concentrations retrieved from closed contexts, morphometric data from archaeological phytoliths compared with similar data from reference material may allow taxonomic attribution. Observer variation is one aspect of phytolith morphometry that has received little attention but may be an important source of error, and hence cause of potential misidentification of plant remains. SCOPE: To investigate inter- and intra-observer variation in phytolith morphometry, eight researchers (observers) from different laboratories measured 50 samples each from three phytolith morphotypes, Bilobate, Bulliform flabellate and Elongate dendritic, three times, under the auspices of the International Committee for Phytolith Morphometrics (ICPM). METHODS: Data for 17 size and shape variables were collected for each phytolith by manually digitising a phytolith outline (mask) from a photograph, followed by measurement of the mask with open-source morphometric software. KEY RESULTS: Inter-observer variation ranged from 0 to 23% difference from the mean of all observers. Intra-observer variation ranged from 0 to 9% difference from the mean of individual observers per week. Inter- and intra-observer variation was generally higher among inexperienced researchers. CONCLUSIONS: Scaling errors were a major cause of variation and occurred more with less experienced researchers, which is likely related to familiarity with data collection. The results indicate that inter- and intra-observer variation can be substantially reduced by providing clear instructions for and training with the equipment, photo capturing, software, data collection and data cleaning. In this paper, the ICPM provides recommendations to minimise variation.Advances in automatic data collection may eventually reduce inter- and intra-observer variation, but until this is common practice, the ICPM recommends that phytolith morphometric analyses adhere to standardised guidelines to assure that measured phytolith variables are accurate, consistent and comparable between different researchers and laboratories.

Exploring discrepancies in muscle analysis with ImageJ: understanding the impact of tool selection on echo intensity and muscle area measurements.

PURPOSE: The aim was to compare the use of different tools within the ImageJ program (polygon vs. segmented line) and their impact on the calculation of muscle area and echo intensity (EI) values in ultrasound imaging of the vastus lateralis muscle. METHODS: Thirteen volunteers participated in this study. Ultrasound images of the vastus lateralis muscle were acquired using 2D B-mode ultrasonography and analyzed using both the polygon and segmented line tools by the same evaluator. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) assessed the tools' reliability. Bland-Altman plots were employed to verify the agreement between measurements, and linear regression analysis determined proportional bias. A paired t-test was conducted to analyze differences between the tools. RESULTS: The reliability between tools for muscle area calculation was weak (r = 0.000; CV = 138.03 ± 0.34%), while it was excellent for EI (r = 0.871; CV = 15.19 ± 2.96%). The Bland-Altman plots indicated a large bias for muscle area (d = 195.2%) with a proportional bias (p < 0.001). For EI, the bias was (d = 15.2) with proportional bias (p = 0.028). The paired t-test revealed significant differences between the tools for area (p < 0.001) but not for EI (p = 0.060). CONCLUSION: The study found significant differences in measurements obtained with the polygon and segmented line tools in ImageJ, with the polygon tool showing higher values for muscle area and lower values for EI.

Automated workflows using Quantitative Colour Pattern Analysis (QCPA): a guide to batch processing and downstream data analysis.

Animal and plant colouration presents a striking dimension of phenotypic variation, the study of which has driven general advances in ecology, evolution, and animal behaviour. Quantitative Colour Pattern Analysis (QCPA) is a dynamic framework for analysing colour patterns through the eyes of non-human observers. However, its extensive array of user-defined image processing and analysis tools means image analysis is often time-consuming. This hinders the full use of analytical power provided by QCPA and its application to large datasets. Here, we offer a robust and comprehensive batch script, allowing users to automate many QCPA workflows. We also provide a complimentary set of useful R scripts for downstream data extraction and analysis. The presented batch processing extension will empower users to further utilise the analytical power of QCPA and facilitate the development of customised semi-automated workflows. Such quantitatively scaled workflows are crucial for exploring colour pattern spaces and developing ever-richer frameworks for analysing organismal colouration accounting for visual perception in animals other than humans. These advances will, in turn, facilitate testing hypotheses on the function and evolution of vision and signals at quantitative and qualitative scales, which are otherwise computationally unfeasible. Supplementary Information: The online version contains supplementary material available at 10.1007/s10682-024-10291-7.

Development of a High-Throughput Pipeline to Characterize Microglia Morphological States at a Single-Cell Resolution.

As rapid responders to their environments, microglia engage in functions that are mirrored by their cellular morphology. Microglia are classically thought to exhibit a ramified morphology under homeostatic conditions which switches to an ameboid form during inflammatory conditions. However, microglia display a wide spectrum of morphologies outside of this dichotomy, including rod-like, ramified, ameboid, and hypertrophic states, which have been observed across brain regions, neurodevelopmental timepoints, and various pathological contexts. We applied dimensionality reduction and clustering to consider contributions of multiple morphology measures together to define a spectrum of microglial morphological states in a mouse dataset that we used to demonstrate the utility of our toolset. Using ImageJ, we first developed a semiautomated approach to characterize 27 morphology features from hundreds to thousands of individual microglial cells in a brain region-specific manner. Within this pool of features, we defined distinct sets of highly correlated features that describe different aspects of morphology, including branch length, branching complexity, territory span, and circularity. When considered together, these sets of features drove different morphological clusters. Our tools captured morphological states similarly and robustly when applied to independent datasets and using different immunofluorescent markers for microglia. We have compiled our morphology analysis pipeline into an accessible, easy-to-use, and fully open-source ImageJ macro and R package that the neuroscience community can expand upon and directly apply to their own analyses. Outcomes from this work will supply the field with new tools to systematically evaluate the heterogeneity of microglia morphological states across various experimental models and research questions.

PCP auto count: a novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting.

Introdution: During development, planes of cells give rise to complex tissues and organs. The proper functioning of these tissues is critically dependent on proper inter- and intra-cellular spatial orientation, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity, investigators must often manually measure cell orientations, which is a time-consuming endeavor. To automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called PCP Auto Count (PCPA). Methods: PCPA analyzes binary images and identifies "chunks" of white pixels that contain "caves" of infiltrated black pixels. For validation, inner ear sensory epithelia including cochleae and utricles from mice were immunostained for ßII-spectrin and imaged with a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear hair cell angle measurements for accuracy using a one to five agreement scale. For utricle samples, PCPA derived measurements were directly compared against manually derived angle measurements and the concordance correlation coefficient (CCC) and Bland-Altman limits of agreement were calculated. PCPA was also tested against previously published images examining PCP in various tissues and across various species suggesting fairly broad utility. Results: PCPA was able to recognize and count 99.81% of cochlear hair cells, and was able to obtain ideally accurate planar cell polarity measurements for at least 96% of hair cells. When allowing for a <10° deviation from "perfect" measurements, PCPA's accuracy increased to 98%-100% for all users and across all samples. When PCPA's measurements were compared with manual angle measurements for E17.5 utricles there was negligible bias (<0.5°), and a CCC of 0.999. Qualitative examination of example images of Drosophila ommatidia, mouse ependymal cells, and mouse radial progenitors revealed a high level of accuracy for PCPA across a variety of stains, tissue types, and species. Discussion: Altogether, the data suggest that the PCPA plug-in suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.

Long-Term Outcomes of Adalimumab Treatment in Conventional Treatment-Resistant Serpiginous Choroiditis.

BACKGROUND/OBJECTIVES: To investigate the long-term efficacy and safety of adalimumab(ADA) in the treatment of patients with serpiginous choroiditis (SC) refractory to conventional therapy through quantitative parameters. SUBJECTS/METHODS: A retrospective analysis was conducted on patients diagnosed with SC clinically and through fundus autofluorescence(FAF). Patients receiving ADA treatment were included. Demographic and clinical characteristics of the patients, association with tuberculosis (TB) infection, number of immunosuppressive therapies, recurrences, best corrected visual acuity (BCVA) change, and ADA-related side effects were recorded. The progression rate before and after ADA was calculated based on the area involved by FAF. RESULTS: Sixteen eyes of 8 patients (3 female/5 male) were enrolled to the study. The median (IQR) age was 53.5 (16.5) years. Diagnosis was SC in 4, ampiginous choroiditis in 3, and TB-related serpiginous-like choroiditis in 1 patient. Peripapillary involvement was present in 10 of 16 eyes. The area involved by FAF continued to progress under ADA treatment, however the progression rate was decreased (p = 0.143).The BCVA was preserved (p = 0.772). The number of systemic and local treatments decreased with ADA (p = 0.025 and 0.019, respectively). Additionally, the number of recurrences was reduced with ADA (p = 0.002). Median (IQR) follow-up was 45(28.75) months. Two patients experienced ADA-related side effects (pulmonary TBand rash). CONCLUSIONS: Our findings suggest a promising role for ADA in halting the progression of SC and have implications for improving outcomes. Despite the evidence in the literature at the level of case reports, ADA can be used effectively with close monitoring for potential risks.

PatternJ: an ImageJ toolset for the automated and quantitative analysis of regular spatial patterns found in sarcomeres, axons, somites, and more.

Regular spatial patterns are ubiquitous forms of organization in nature. In animals, regular patterns can be found from the cellular scale to the tissue scale, and from early stages of development to adulthood. To understand the formation of these patterns, how they assemble and mature, and how they are affected by perturbations, a precise quantitative description of the patterns is essential. However, accessible tools that offer in-depth analysis without the need for computational skills are lacking for biologists. Here, we present PatternJ, a novel toolset to analyze regular one-dimensional patterns precisely and automatically. This toolset, to be used with the popular imaging processing program ImageJ/Fiji, facilitates the extraction of key geometric features within and between pattern repeats in static images and time-lapse series. We validate PatternJ with simulated data and test it on images of sarcomeres from insect muscles and contracting cardiomyocytes, actin rings in neurons, and somites from zebrafish embryos obtained using confocal fluorescence microscopy, STORM, electron microscopy, and brightfield imaging. We show that the toolset delivers subpixel feature extraction reliably even with images of low signal-to-noise ratio. PatternJ's straightforward use and functionalities make it valuable for various scientific fields requiring quantitative one-dimensional pattern analysis, including the sarcomere biology of muscles or the patterning of mammalian axons, speeding up discoveries with the bonus of high reproducibility.

Detection of Stress-Induced Changes in Subcellular Protein Distribution.

Proteins often show alterations in their subcellular localization with changing environmental conditions; transcription factors enter the nucleus or are actively removed from the nucleus; some even bind to endo-membranes by conditional membrane anchors; and other proteins and mRNA arrange in RNA granules. These are some examples of the complex regulation of subcellular localization, which often depends on posttranslational modifications and is triggered by environmental stressors. The challenge is the precise identification of the compartments, the quantitative analysis of proteins, which reside in multiple compartments, and their transport dynamics. Therefore, appropriate compartment markers and routines for a reproducible quantitative workflow are required.

Reactive Oxygen Species (ROS) Measurement in Arabidopsis Guard Cells in Response to Biotic and Abiotic Stresses.

One of the major plant stress level indicators is reactive oxygen species (ROS). They have been known to play a central role in regulating plant responses to various environmental stresses. This book chapter specifically covers abiotic stress induced by a drought hormone abscisic acid and biotic stress induced by Pseudomonas syringe DC3000 on single cell-type guard cells. We describe in detail the measurement of ROS production starting from sample preparation to data analysis by fluorescence intensity acquisition using ImageJ software. We discussed the problems faced while performing the experiment and addressed how to overcome them by providing specific guidelines to ensure high quality repeatable data.

Analysis of Surface Expression of NMDAR Subunits in Primary Hippocampal Neurons.

The expression and activity of ionotropic glutamate receptors control signal transduction at the excitatory synapses in the CNS. The NMDAR comprises two obligatory GluN1 subunits and two GluN2 or GluN3 subunits in different combinations. Each GluN subunit consists of four domains: the extracellular amino-terminal and agonist-binding domains, the transmembrane domain, and the intracellular C-terminal domain (CTD). The CTD interaction with various classes of intracellular proteins is critical for trafficking and synaptic localization of NMDARs. Amino acid mutations or the inclusion of premature stop codons in the CTD could contribute to the emergence of neurodevelopmental and neuropsychiatric disorders. Here, we describe the method of preparing primary hippocampal neurons and lentiviral particles expressing GluN subunits that can be used as a model to study cell surface expression and synaptic localization of NMDARs. We also show a simple method of fluorescence immunostaining of eGFP-tagged GluN2 subunits and subsequent microscopy technique and image analysis to study the effects of disease-associated mutations in the CTDs of GluN2A and GluN2B subunits.

Machine learning-based 3D segmentation of mitochondria in polarized epithelial cells.

Mitochondria are dynamic organelles that alter their morphological characteristics in response to functional needs. Therefore, mitochondrial morphology is an important indicator of mitochondrial function and cellular health. Reliable segmentation of mitochondrial networks in microscopy images is a crucial initial step for further quantitative evaluation of their morphology. However, 3D mitochondrial segmentation, especially in cells with complex network morphology, such as in highly polarized cells, remains challenging. To improve the quality of 3D segmentation of mitochondria in super-resolution microscopy images, we took a machine learning approach, using 3D Trainable Weka, an ImageJ plugin. We demonstrated that, compared with other commonly used methods, our approach segmented mitochondrial networks effectively, with improved accuracy in different polarized epithelial cell models, including differentiated human retinal pigment epithelial (RPE) cells. Furthermore, using several tools for quantitative analysis following segmentation, we revealed mitochondrial fragmentation in bafilomycin-treated RPE cells.

Correlation Between ImageJ and Conventional Manual Scoring Methods for Programmed Death-Ligand 1 Immuno-Histochemically Stained Sections.

Background: One of the most frequently used methods for quantifying PD-L1 (programmed cell death-ligand 1) expression in tumor tissue is IHC (immunohistochemistry). This may predict the patient's response to anti-PD1/PD-L1 therapy in cancer. Methods: ImageJ software was used to score IHC-stained sections for PD-L1 and compare the results with the conventional manual method. Results: In diffuse large B cell lymphoma, no significant difference between the scores obtained by the conventional method and ImageJ scores obtained using the option "RGB" or "Brightness/Contrast." On the other hand, a significant difference was found between the conventional and HSB scoring methods. ImageJ faced some challenges in analyzing head and neck squamous cell carcinoma tissues because of tissue heterogenicity. A significant difference was found between the conventional and ImageJ scores using HSB or RGB but not with the "Brightness/Contrast" option. Scores obtained by ImageJ analysis after taking images using 20 × objective lens gave significantly higher readings compared to 40 × magnification. A significant difference between camera-captured images' scores and scanner whole slide images' scores was observed. Conclusion: ImageJ can be used to score homogeneous tissues. In the case of highly heterogeneous tissues, it is advised to use the conventional method rather than ImageJ scoring.

Quantification of vascular changes in macular telangiectasia type 2 with AngioTool software.

PURPOSE: To compare AngioTool (AT) vascular parameters (VP) between MacTel2 eyes and normal eyes. Secondary outcome measures were to correlate VP with BCVA and to analyze VP between various grades of Simple MacTel Classification. METHODS: This is a retrospective study. SD OCTA images of the superficial vascular complex (SVC) and deep capillary complex (DVC) were exported into Image J and AT. The explant area (EA), vessel area (VA), vessel percentage area (VPA), total number of junctions (TNJ), junction density (JD), total vessel length (TVL), average vessel length (AVL), total number of endpoints (TNE) and mean E lacunarity (MEL) were studied. RESULTS: Group 1 had 120 MacTel2 eyes. Group 2 had 60 age-matched normal eyes. All VP were significantly different between the two groups except EA and TNE in both complexes. None of the VP had a correlation with BCVA. Interquadrant analysis (IQA) in SVC and DVC showed statistical significance in VPA, AVL and JD and in AVL, TNE, JD, VPA respectively. Post hoc analysis in SVC and DVC showed statistical significance in TNJ, JD, TVL and AVL between grade 1 and grade 3, and in VA, VPA, TNJ, JD, TVL and MEL between grade 0 and grade 3 respectively. CONCLUSION: VP were affected in MacTel2 eyes. VP did not correlate with BCVA. Occurrence of pigmentation is an important event in the progression of disease. AT may provide quantitative markers to measure disease progression.

PD-L1 Expression in Colorectal Carcinoma Correlates with the Immune Microenvironment.

INTRODUCTION/BACKGROUND: Colorectal carcinoma (CRC) is a common malignancy, with its diverse clinical, pathological, and molecular features. The immune microenvironment of a tumor comprises of interplay between various cells and molecules, and has a significant role in deciding the tumor behavior and overall prognosis. PD-L1 (programmed cell death ligand-1) has been implicated in the regulation of the tumor immune microenvironment (TIME). There is limited data regarding the correlation of PD-L1 expression with immune cell profile in CRCs, especially in the Indian setting. The study aimed to assess the PD-L1 expression in CRC tumor cells and its association with TIME, mismatch repair (MMR), and various other clinicopathological parameters. METHODS: This is a hospital-based, cross-sectional observational study. PD-L1 expression was assessed at the protein level by immunohistochemistry and mRNA level by qRT-PCR. Immune cell markers (CD4, CD8, CD20, FOXP3, and CD163) were interpreted using the ImageJ Fiji platform. RESULTS: Of the 104 cases, 21% were PD-L1 positive and were more common in right-sided CRCs. PD-L1 positive cases showed significantly higher concentrations of all T-cell subsets (CD4+ , CD8+ , and FOXP3+), CD20+ B-cells, and CD163+ macrophages were noted. No statistical significance was seen between PD-L1 expression with clinical profile, pathological subtype, grade or stage, mismatch repair status (proficient vs deficient), and survival. CONCLUSIONS: The present study showed a relatively lower frequency of PD-L1 in CRC from the Eastern Indian cohort. The immune cell concentration in the present study was calculated using image analysis-based objectivised methods. Significant correlation of PD-L1 expression in tumor cells with the tumor-infiltrating immune cells indicated its crucial role in the pathobiology of CRC especially by regulating the TIME. Considering the therapeutic implication of PD-L1 in various malignancies, it may be one of the crucial therapeutic targets in a proportion of cases.

A potential novel technique for measurement of pulp volume on periapical radiography: A pilot study.

Pulp volume can be assessed during dental treatment. Three-dimensional imaging techniques are not routinely used for this purpose because of high radiation doses. This study aimed to develop a novel method to measure pulp volume using periapical radiography. In this study, cone-beam computed tomography (CBCT) was used as a reference method. Periapical radiography and CBCTs obtained from the same patients (n = 32) were recorded. Pulp volume was determined by observing the density differences between the pulp and peripheral structures using ImageJ. A method of graph and volume calculation was developed for each tooth. The Shapiro-Wilk test and Mann-Whitney U test were used to show normality and non-normal distributions. The Bland-Altman plot was used to show the scattering of the mean versus difference values of the measurements of the two methods used to calculate the pulp volume. Normality was evaluated using the Shapiro-Wilk test. CBCT measurements are normally distributed (p = 0.307), while ImageJ is not normally distributed (p = 0.027). Therefore, the mean difference between the two groups was analyzed using the nonparametric Mann-Whitney U test. There was a statistically significant difference between the CBCT and ImageJ measurements (p = 0.01). According to Spearman's correlation analysis, the results obtained from the novel method were moderately correlated with those obtained from the reference method (r = 0.444). The results of this study indicated that a novel method-based Java software can be used to calculate pulp volume using low-dose radiation containing periapical radiography.

Analysis of Unique Motility of the Unicellular Green Alga Chlamydomonas reinhardtii at Low Temperatures down to -8 °C.

Previous studies of motility at low temperatures in Chlamydomonas reinhardtii have been conducted at temperatures of up to 15 °C. In this study, we report that C. reinhardtii exhibits unique motility at a lower temperature range (-8.7 to 1.7 °C). Cell motility was recorded using four low-cost, easy-to-operate observation systems. Fast Fourier transform (FFT) analysis at room temperature (20-27 °C) showed that the main peak frequency of oscillations ranged from 44 to 61 Hz, which is consistent with the 60 Hz beat frequency of flagella. At lower temperatures, swimming velocity decreased with decreasing temperature. The results of the FFT analysis showed that the major peak shifted to the 5-18 Hz range, suggesting that the flagellar beat frequency was decreasing. The FFT spectra had distinct major peaks in both temperature ranges, indicating that the oscillations were regular. This was not affected by the wavelength of the observation light source (white, red, green or blue LED) or the environmental spatial scale of the cells. In contrast, cells in a highly viscous (3.5 mPa·s) culture at room temperature showed numerous peaks in the 0-200 Hz frequency band, indicating that the oscillations were irregular. These findings contribute to a better understanding of motility under lower-temperature conditions in C. reinhardtii.

Skeletal Muscle Echo Intensity Values Differ Significantly across Ultrasound Parameter Settings.

Echo intensity determined by muscle ultrasound has been proposed as an efficient method for the assessment of muscle quality. The influence of changing ultrasound parameter settings on echo intensity values was assessed using a standardized approach. In this repeated measures cross-sectional study, sixteen repeated scans of rectus femoris, gracilis, and rectus abdominis were taken in 21 middle-aged persons with a portable Mindray M7 premium ultrasound machine equipped with a linear 5.0-10.0 MHz transducer. The settings of three parameters were fixed: gain, depth, and frequency. The settings of the following adjustable parameters were changed over their entire range: dynamic range, gray map, line density, persistence, and IClear. Repeated measures analyses were performed to evaluate the effect of changing the settings on echo intensity values. In all three muscles, dynamic range, gray map, and IClear correlated significantly (rrm-values ranging between -0.86 and 0.45) with echo intensity. In all three muscles, the echo intensity values differed significantly across the dynamic range (p < 0.013), gray map (p < 0.003), and IClear (p < 0.003). In middle-aged subjects, echo intensity values of lower limb and trunk muscles are significantly related to ultrasound parameters and significantly differ across their respective setting range. For the assessment of muscle quality through ultrasound, it is suggested to fix parameter settings within their midrange in order to minimize the effect of setting-dependent factors on EI values.

Computational and Translational Fractal-Based Analysis in the Translational Neurosciences: An Overview.

After the previous sections on "Fractals: What and Why?," the last section of this book covers the software tools necessary to perform computational fractal-based analysis, with special emphasis on its applications into the neurosciences. The use of ImageJ and MATLAB, as well as other software packages, is reviewed. The current and future applications of fractal modeling in bioengineering and biotechnology are discussed as well. Perspectives on the translation of merging fractals with artificial intelligence-based methods with the final aim of pattern discrimination in neurological diseases by means of a unified fractal model of the brain are also given. Moreover, some new translational applications of fractal analysis to the neurosciences are presented, including eye tracking analysis, cognitive neuroscience, and music.

ImageJ in Computational Fractal-Based Neuroscience: Pattern Extraction and Translational Research.

To explore questions asked in neuroscience, neuroscientists rely heavily on the tools available. One such toolset is ImageJ, open-source, free, biological digital image analysis software. Open-source software has matured alongside of fractal analysis in neuroscience, and today ImageJ is not a niche but a foundation relied on by a substantial number of neuroscientists for work in diverse fields including fractal analysis. This is largely owing to two features of open-source software leveraged in ImageJ and vital to vigorous neuroscience: customizability and collaboration. With those notions in mind, this chapter's aim is threefold: (1) it introduces ImageJ, (2) it outlines ways this software tool has influenced fractal analysis in neuroscience and shaped the questions researchers devote time to, and (3) it reviews a few examples of ways investigators have developed and used ImageJ for pattern extraction in fractal analysis. Throughout this chapter, the focus is on fostering a collaborative and creative mindset for translating knowledge of the fractal geometry of the brain into clinical reality.

MARTin-an open-source platform for microarray analysis.

Background: Microarray technology has brought significant advancements to high-throughput analysis, particularly in the comprehensive study of biomolecular interactions involving proteins, peptides, and antibodies, as well as in the fields of gene expression and genotyping. With the ever-increasing volume and intricacy of microarray data, an accurate, reliable and reproducible analysis is essential. Furthermore, there is a high level of variation in the format of microarrays. This not only holds true between different sample types but is also due to differences in the hardware used during the production of the arrays, as well as the personal preferences of the individual users. Therefore, there is a need for transparent, broadly applicable and user-friendly image quantification techniques to extract meaningful information from these complex datasets, while also addressing the challenges posed by specific microarray and imager formats, which can flaw analysis and interpretation. Results: Here we introduce MicroArray Rastering Tool (MARTin), as a versatile tool developed primarily for the analysis of protein and peptide microarrays. Our software provides state-of-the-art methodologies, offering researchers a comprehensive tool for microarray image quantification. MARTin is independent of the microarray platform used and supports various configurations including high-density formats and printed arrays with significant x and y offsets. This is made possible by granting the user the ability to freely customize parts of the application to their specific microarray format. Thanks to built-in features like adaptive filtering and autofit, measurements can be done very efficiently and are highly reproducible. Furthermore, our tool integrates metadata management and integrity check features, providing a straightforward quality control method, along with a ready-to-use interface for in-depth data analysis. This not only promotes good scientific practice in the field of microarray analysis but also enhances the ability to explore and examine the generated data. Conclusion: MARTin has been developed to empower its users with a reliable, efficient, and intuitive tool for peptidomic and proteomic array analysis, thereby facilitating data-driven discovery across disciplines. Our software is an open-source project freely available via the GNU Affero General Public License licence on GitHub.