Fluorescence Microscopy Images Research Articles

Live Cell Fluorescence Microscopy—An End-to-End Workflow for High-Throughput Image and Data Analysis

Abstract Fluorescence microscopy images of biological samples contain valuable information but require rigorous analysis for accurate and reliable determination of changes in protein localization, fluorescence intensity and morphology of the studied objects. Traditionally, cells for microscopy are immobilized using chemicals, which can introduce stress. Analysis often focuses only on colocalization and involves manual segmentation and measurement, which are time-consuming and can introduce bias. Our new workflow addresses these issues by gently immobilizing cells using a small agarose block on a microscope coverslip. This approach is suitable for cell-walled cells (yeast, fungi, plants, bacteria), facilitates their live imaging under conditions close to their natural environment and enables the addition of chemicals during time-lapse experiments. The primary focus of the protocol is on the presented analysis workflow, which is applicable to virtually any cell type—we describe cell segmentation using the Cellpose software followed by automated analysis of a multitude of parameters using custom-written Fiji (ImageJ) macros. The results can be easily processed using the provided R markdown scripts or available graphing software. Our method facilitates unbiased batch analysis of large datasets, improving the efficiency and accuracy of fluorescence microscopy research. The reported sample preparation protocol and Fiji macros were used in our recent publications: Microbiol Spectr (2022), DOI: 10.1128/spectrum.01961-22; Microbiol Spectr (2022), DOI: 10.1128/spectrum.02489-22; J Cell Sci (2023), DOI: 10.1242/jcs.260554.

Apache is a neuronal player in autophagy required for retrograde axonal transport of autophagosomes

Neurons are dependent on efficient quality control mechanisms to maintain cellular homeostasis and function due to their polarization and long-life span. Autophagy is a lysosomal degradative pathway that provides nutrients during starvation and recycles damaged and/or aged proteins and organelles. In neurons, autophagosomes constitutively form in distal axons and at synapses and are trafficked retrogradely to the cell soma to fuse with lysosomes for cargo degradation. How the neuronal autophagy pathway is organized and controlled remains poorly understood. Several presynaptic endocytic proteins have been shown to regulate both synaptic vesicle recycling and autophagy. Here, by combining electron, fluorescence, and live imaging microscopy with biochemical analysis, we show that the neuron-specific protein APache, a presynaptic AP-2 interactor, functions in neurons as an important player in the autophagy process, regulating the retrograde transport of autophagosomes. We found that APache colocalizes and co-traffics with autophagosomes in primary cortical neurons and that induction of autophagy by mTOR inhibition increases LC3 and APache protein levels at synaptic boutons. APache silencing causes a blockade of autophagic flux preventing the clearance of p62/SQSTM1, leading to a severe accumulation of autophagosomes and amphisomes at synaptic terminals and along neurites due to defective retrograde transport of TrkB-containing signaling amphisomes along the axons. Together, our data identify APache as a regulator of the autophagic cycle, potentially in cooperation with AP-2, and hypothesize that its dysfunctions contribute to the early synaptic impairments in neurodegenerative conditions associated with impaired autophagy.

Development and In vitro and In vivo efficacy investigation of multifunctional, targeted, theranostic liposomes for imaging and photodynamic therapy of glioblastoma

The challenge of effectively diagnosing and treating glioblastoma, which has a high incidence and low survival rate, remains unresolved. Consequently, research efforts have increased to develop specific and effective diagnostic and therapeutic agents. In this study, N-acetyl glucosamine-modified, IR780-encapsulated, PEG-coated, nanosized, 68Ga-labeled, neutral, and positively charged theranostic liposomes were developed for GLUT1 targeting for PET/CT and NIR imaging and PDT/PTT of GBM. Characterization, in vitro release profile, stability, radiolabeling efficiency, and labeling stability were evaluated. In vitro cell uptake studies were conducted in RG2 and U87 GBM cell lines, NIR images were obtained in the RG2 and U87 cell lines, and fluorescence microscope images were obtained in the RG2 cell line. PDT/PTT activities were assessed after exposure to NIR light (808 nm, 0.8–1 W/cm2). Cytotoxicity was studied in RG2, U87, and L929 cell lines. In vivo studies were performed in GBM model nude mice. The liposomes had an optimum mean particle size (181–193 nm), high encapsulation efficiency (79–84 %), and zeta potential (−5 to −7 mV). The maximum 68Ga labeling efficiency was achieved with an incubation at 80 °C, pH: 3.5, and 5 min. Liposomes remained stable for 30 days at 4 °C. While the intracellular uptake of targeted formulations was higher than non-targeted ones, it was similar for positively charged and neutral formulations. In U87 and RG2 cell lines, cell viability remained above 50 % for liposomal formulations at a concentration of 1.5 μg/mL exposed to NIR light (808 nm, 0.8–1 W/cm2). The formulations showed significant PDT/PTT activity under NIR light and lower cytotoxicity in the L929 than in RG2 and U87 cell lines. In vivo bioluminescence images and biodistribution studies in U87 tumor-bearing nude mice revealed higher tumor uptake of targeted, IR780-encapsulated liposomal formulations in tumor tissue compared to non-targeted ones. Liposomal formulations showed higher tumor uptake than the free IR780 solution. The data suggest that targeted liposomes have potential as theranostic agents for PDT/PTT and multifunctional glioblastoma imaging.

Soft Ca2+-induced pea protein gels through mild heating: Controlling elasticity and fracturing behavior

With the rising demand for plant-based protein, understanding the formation of pea protein gels induced by calcium is important for manufacturing of food products with appealing textural properties. Moreover, the moderate temperature processing is becoming of greater and greater interest, based on conditions used for food fermentation. The objective of this study is to understand the structural and rheology behaviour of calcium-induced pea protein aggregates at moderate plateau temperatures i.e. in the range 30–45 °C with holding times from 0 to 60,000s. Based on the combination of confocal fluorescence microscopy (CLSM) imaging, rheology and IR spectroscopy, our results show that despite the relatively low temperature range, thermal agitation not only allows for partial protein particle dissolution, but also induces interactions leading to soft gel formation. Within a shorter holding time (0–600s) range, higher Gʹ/Gʹ0 (t) were observed at 40 °C and 45 °C, the end elastic modulus reaching for any parameters tested at least 1000 Pa. As the holding time was extended to 60,000s, the effect of temperature became less pronounced, i.e., all gels reached similar high values of Gʹ even for the low temperature of 30 °C, which is nearly 3–4 times higher than that formed at 600s holding time (at the temperature plateau). IR spectroscopy measurements showed that the protein secondary structure remained stable within the holding time ranging from 0s to 6,000s, while significant but moderate changes were monitored when extending the holding time to 60,000s, which correlated with a more progressive yielding observed in rheology, pointing to the formation of apparently more fragile physical gels. The combination of the techniques shows that the gel formation and strengthening mainly originated from rearrangements at the colloidal level leading to a denser network, while molecular rearrangements only intervened at the longest holding times (i.e, 60,000s).

Unlocking the potential of large-scale 3D imaging with tissue clearing techniques.

The three-dimensional (3D) anatomical structure of living organisms is intrinsically linked to their functions, yet modern life sciences have not fully explored this aspect. Recently, the combination of efficient tissue clearing techniques and light-sheet fluorescence microscopy (LSFM) for rapid 3D imaging has improved access to 3D spatial information in biological systems. This technology has found applications in various fields, including neuroscience, cancer research, and clinical histopathology, leading to significant insights. It allows imaging of entire organs or even whole bodies of animals and humans at multiple scales. Moreover, it enables a form of spatial omics by capturing and analyzing cellome information, which represents the complete spatial organization of cells. While current 3D imaging of cleared tissues has limitations in obtaining sufficient molecular information, emerging technologies such as multi-round tissue staining and super-multicolor imaging are expected to address these constraints. 3D imaging using tissue clearing and light-sheet microscopy thus offers a valuable research tool in the current and future life sciences for acquiring and analyzing large-scale biological spatial information.

Classification of Single‐Photon Emitters in Confocal Fluorescence Microscope Images by Deep Convolutional Neural Networks

Classification of Single‐Photon Emitters in Confocal Fluorescence Microscope Images by Deep Convolutional Neural Networks

Local Mean Suppression Filter for Effective Background Identification in Fluorescence Images.

We present an easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground. The pixel-wise filtering is based on comparison of the pixel intensity with the mean intensity of pixels in its local neighborhood. The pixel is given a background or foreground label depending on whether its intensity is less than or greater than the mean respectively. Multiple labels are generated for the same pixel by computing mean expression values by varying neighborhood size. These labels are accumulated to decide the final pixel label. We demonstrate that the performance of our filter favorably compares with state-of-the-art image processing, machine learning, and deep learning methods. We present three use cases that demonstrate its effectiveness, and also show how it can be used in multiplexed fluorescence imaging contexts and as a denoising step in image segmentation. A fast implementation of the filter is available in Python 3 on GitHub .

ProDiVis: a method to normalize fluorescence signal localization in 3D specimens.

A common problem in confocal microscopy is the decrease in intensity of excitation light and emission signal from fluorophores as they travel through 3D specimens, resulting in decreased signal detected as a function of depth. Here, we report a visualization program compatible with widely used fluorophores in cell biology to facilitate image interpretation of differential protein disposition in 3D specimens. Glioblastoma cell clusters were fluorescently labeled for mitochondrial complex I (COXI), P2X7 receptor (P2X7R), β-Actin, Ki-67, and DAPI. Each cell cluster was imaged using a laser scanning confocal microscope. We observed up to ∼70% loss in fluorescence signal across the depth in Z-stacks. This progressive underrepresentation of fluorescence intensity as the focal plane deepens hinders an accurate representation of signal location within a 3D structure. To address these challenges, we developed ProDiVis: a program that adjusts apparent fluorescent signals by normalizing one fluorescent signal to a reference signal at each focal plane. ProDiVis serves as a free and accessible, unbiased visualization tool to use in conjunction with fluorescence microscopy images and imaging software.

Accurate segmentation of intracellular organelle networks using low-level features and topological self-similarity.

Intracellular organelle networks (IONs) such as the endoplasmic reticulum (ER) network and the mitochondrial (MITO) network serve crucial physiological functions. The morphology of these networks plays a critical role in mediating their functions. Accurate image segmentation is required for analyzing the morphology and topology of these networks for applications such as molecular mechanism analysis and drug target screening. So far, however, progress has been hindered by their structural complexity and density. In this study, we first establish a rigorous performance baseline for accurate segmentation of these organelle networks from fluorescence microscopy images by optimizing a baseline U-Net model. We then develop the multi-resolution encoder (MRE) and the hierarchical fusion loss (Lhf) based on two inductive components, namely low-level features and topological self-similarity, to assist the model in better adapting to the task of segmenting IONs. Empowered by MRE and Lhf, both U-Net and Pyramid Vision Transformer (PVT) outperform competing state-of-the-art models such as U-Net++, HR-Net, nnU-Net, and TransUNet on custom datasets of the ER network and the MITO network, as well as on public datasets of another biological network, the retinal blood vessel network. In addition, integrating MRE and Lhf with models such as HR-Net and TransUNet also enhances their segmentation performance. These experimental results confirm the generalization capability and potential of our approach. Furthermore, accurate segmentation of the ER network enables analysis that provides novel insights into its dynamic morphological and topological properties. Code and data are openly accessible at https://github.com/cbmi-group/MRE.

Organelle landscape analysis using a multiparametric particle-based method.

Organelles have unique structures and molecular compositions for their functions and have been classified accordingly. However, many organelles are heterogeneous and in the process of maturation and differentiation. Because traditional methods have a limited number of parameters and spatial resolution, they struggle to capture the heterogeneous landscapes of organelles. Here, we present a method for multiparametric particle-based analysis of organelles. After disrupting cells, fluorescence microscopy images of organelle particles labeled with 6 to 8 different organelle markers were obtained, and their multidimensional data were represented in two-dimensional uniform manifold approximation and projection (UMAP) spaces. This method enabled visualization of landscapes of 7 major organelles as well as the transitional states of endocytic organelles directed to the recycling and degradation pathways. Furthermore, endoplasmic reticulum-mitochondria contact sites were detected in these maps. Our proposed method successfully detects a wide array of organelles simultaneously, enabling the analysis of heterogeneous organelle landscapes.

Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues.

The geometric shape and arrangement of individual cells play a role in shaping organ functions. However, analyzing multicellular features and exploring their connectomes in centimeter-scale plant organs remain challenging. Here, we established a set of frameworks named Large-Volume Fully Automated Cell Reconstruction (LVACR), enabling the exploration of three-dimensional (3D) cytological features and cellular connectivity in plant tissues. Through benchmark testing, our framework demonstrated superior efficiency in cell segmentation and aggregation, successfully addressing the inherent challenges posed by light sheet fluorescence microscopy (LSFM) imaging. Using LVACR, we successfully established a cell atlas of different plant tissues. Cellular morphology analysis revealed differences of cell clusters and shapes in between different poplar (P. simonii Carr. and P. canadensis Moench.) seeds, whereas topological analysis revealed that they maintained conserved cellular connectivity. Furthermore, LVACR spatiotemporally demonstrated an initial burst of cell proliferation, accompanied by morphological transformations at an early stage in developing the shoot apical meristem. During subsequent development, cell differentiation produced anisotropic features, thereby resulting in various cell shapes. Overall, our findings provided valuable insights into the precise spatial arrangement and cellular behavior of multicellular organisms, thus enhancing our understanding of the complex processes underlying plant growth and differentiation.

A-scan fluorescence microscopy for rapid cross-sectional imaging

This paper presents a microscopy technique that can perform snapshot depth resolved optical imaging in the same manner as A-scan in ultrasound imaging and optical coherence tomography. In this technique, a laser line along the axial dimension is used to illuminate a sample to create a fluorescent line object. By transforming the line object along the axial dimension (Z) to a ring image on the lateral dimensions (X-Y) using a full cone mirror, common optics can be used to relay and acquire the ring image precisely. Then, by converting half of the ring image back to a line image using a half cone mirror, the opening side of the half cone mirror allows the line image, which contains the full depth resolved information of the line object, to be taken in one snapshot. This eliminates the requirement of axial scanning in traditional depth resolved imaging techniques such as confocal microscopy to obtain the same information. The technique is demonstrated by imaging fluorescent microspheres of different diameters. This technique offers a simple alternative to traditional depth resolved imaging techniques such as confocal microscopy and light sheet microscopy. It is particularly useful in imaging samples with multiple layers in which multiple A-scans or a few B-scans are sufficient to represent the entire sample.

Exposed Phosphatidylserine as a Biomarker for Clear Identification of Breast Cancer Brain Metastases in Mouse Models.

Brain metastasis is the most common intracranial malignancy in adults. The prognosis is extremely poor, partly because most patients have more than one brain lesion, and the currently available therapies are nonspecific or inaccessible to those occult metastases due to an impermeable blood-tumor barrier (BTB). Phosphatidylserine (PS) is externalized on the surface of viable endothelial cells (ECs) in tumor blood vessels. In this study, we have applied a PS-targeting antibody to assess brain metastases in mouse models. Fluorescence microscopic imaging revealed that extensive PS exposure was found exclusively on vascular ECs of brain metastases. The highly sensitive and specific binding of the PS antibody enables individual metastases, even micrometastases containing an intact BTB, to be clearly delineated. Furthermore, the conjugation of the PS antibody with a fluorescence dye, IRDye 800CW, or a radioisotope, 125I, allowed the clear visualization of individual brain metastases by optical imaging and autoradiography, respectively. In conclusion, we demonstrated a novel strategy for targeting brain metastases based on our finding that abundant PS exposure occurs on blood vessels of brain metastases but not on normal brain, which may be useful for the development of imaging and targeted therapeutics for brain metastases.

A tutorial on optical photothermal infrared (O-PTIR) microscopy.

This tutorial reviews the rapidly growing field of optical photothermal infrared (O-PTIR) spectroscopy and chemical imaging. O-PTIR is an infrared super-resolution measurement technique where a shorter wavelength visible probe is used to measure and map infrared (IR) absorption with spatial resolution up to 30× better than conventional techniques such as Fourier transform infrared and direct IR laser imaging systems. This article reviews key limitations of conventional IR instruments, the O-PTIR technology breakthroughs, and their origins that have overcome the prior limitations. This article also discusses recent developments in expanding multi-modal O-PTIR approaches that enable complementary Raman spectroscopy and fluorescence microscopy imaging, including wide-field O-PTIR imaging with fluorescence-based detection of IR absorption. Various practical subjects are covered, including sample preparation techniques, optimal measurement configurations, use of IR tags/labels and techniques for data analysis, and visualization. Key O-PTIR applications are reviewed in many areas, including biological and biomedical sciences, environmental and microplastics research, (bio)pharmaceuticals, materials science, cultural heritage, forensics, photonics, and failure analysis.

Dual-Modal Near-Infrared Organic Nanoparticles: Integrating Mild Hyperthermia Phototherapy with Fluorescence Imaging.

Our study seeks to develop dual-modal organic-nanoagents for cancer therapy and real-time fluorescence imaging, followed by their pre-clinical evaluation on a murine model. Integrating NIR molecular imaging with nanotechnology, our aim is to improve outcomes for early-stage cutaneous melanoma by offering more effective and less invasive methods. This approach has the potential to enhance both photothermal therapy (PTT) and Sentinel Lymph Node Biopsy (SLNB) procedures for melanoma patients. NIR-797-isothiocyanate was encapsulated in poly(D,L-lactide-co-glycolide) acid (PLGA) nanoparticles (NPs) using a two-step protocol, followed by thorough characterization, including assessing loading efficiency, fluorescence stability, and photothermal conversion. Biocompatibility and cellular uptake were tested in vitro on melanoma cells, while PTT assay, with real-time thermal monitoring, was performed in vivo on tumor-bearing mice under irradiation with an 808 nm laser. Finally, ex vivo fluorescence microscopy, histopathological assay, and TEM imaging were performed. Our PLGA NPs, with a diameter of 270 nm, negative charge, and 60% NIR-797 loading efficiency, demonstrated excellent stability and fluorescence properties, as well as efficient light-to-heat conversion. In vitro studies confirmed their biocompatibility and cellular internalization. In vivo experiments demonstrated their efficacy as photothermal agents, inducing mild hyperthermia with temperatures reaching up to 43.8 °C. Ex vivo microscopy of tumor tissue confirmed persistent NIR fluorescence and uniform distribution of the NPs. Histopathological and TEM assays revealed early apoptosis, immune cell response, ultrastructural damage, and intracellular material debris resulting from combined NP treatment and irradiation. Additionally, TEM analyses of irradiated zone margins showed attenuated cellular damage, highlighting the precision and effectiveness of our targeted treatment approach. Specifically tailored for dual-modal NIR functionality, our NPs offer a novel approach in cancer PTT and real-time fluorescence monitoring, signaling a promising avenue toward clinical translation.

Microplastic retention in green walls for nature-based and decentralized greywater treatment

In wastewater treatment, two issues have recently received increased attention: nature-based solutions for addressing urban water stress through decentralized treatment and re-use; and emerging pollutants such as microplastics (MPs). At the interface of these, this study investigated living green walls for greywater treatment and their potential for MP removal.A large, pilot-scale green wall was irrigated with greywater (a mix of water collected from laundry, dishwasher, bathroom sinks, and synthetic greywater), and effluent from planted and unplanted sections was compared. MPs >50 μm were analyzed using μRaman spectroscopy and supplementary fluorescence microscopy imaging. The green wall proved efficient for the reduction of chemical oxygen demand (COD) (around 80%), removal of total suspended solids (TSS) (around 90%) and MPs, especially for MPs of the non-polar, hydrophobic polymer type polystyrene and MPs sized 100–500 μm. MP removal was improved in the planted (50–60%) compared to the unplanted section (20%), especially for the size fraction 100–500 μm. Physical filtration by the green wall growing media (a mix of perlite with a grain size of 1–5 mm, and coconut fiber), which was further enhanced by plant roots decreasing the effective pore size, can be considered the most important removal mechanism. Charge-mediated adsorption cannot be expected as MPs and growing media mix were both negatively charged at the prevailing water pH (7–8). Fluorescence imaging for MP analysis, using a merged UV/blue light fluorograph, overestimated MP concentrations in greywater (hundreds of MPs per sample were identified by fluorescence imaging versus tens of MPs by μRaman spectroscopy) and would benefit from further improvement before it can be reliably applied as a cheaper and faster alternative methodology for MP analysis.

Oxidation of lipid membrane cholesterol by cholesterol oxidase and its effects on raft model membrane structure

The effects of a peripheral protein – cholesterol oxidase (3β-hydroxysteroid oxidase, ChOx) on the characteristics of model lipid membranes composed of cholesterol, cholesterol:sphingomyelin (1:1), and the raft model composed of DOPC:Chol:SM (1:1:1) were investigated using two membrane model systems: the flat monolayer prepared by the Langmuir technique and the curved model consisting of liposome of the same lipids. The planar monolayers and liposomes were employed to follow membrane cholesterol oxidation to cholestenone catalyzed by ChOx and changes in the lipid membrane structure accompanying this reaction. Changes in the structure of liposomes in the presence of the enzyme were reflected in the changes of hydrodynamic diameter and fluorescence microscopy images, while changes of surface properties of planar membranes were evaluated by grazing incidence X-ray diffraction (GIXD) and Brewster angle microscopy. UV-Vis absorbance measurements confirmed the activity of the enzyme in the tested systems. A better understanding of the interactions between the enzyme and the cell membrane may help in finding alternative ways to decrease excessive cholesterol levels than the common approach of treating hypercholesterolemia with statins, which are not free from undesirable side effects, repeatedly reported in the literature and observed by the patients.

Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images

BackgroundFluorescence microscopy (FM) is an important and widely adopted biological imaging technique. Segmentation is often the first step in quantitative analysis of FM images. Deep neural networks (DNNs) have become the state-of-the-art tools for image segmentation. However, their performance on natural images may collapse under certain image corruptions or adversarial attacks. This poses real risks to their deployment in real-world applications. Although the robustness of DNN models in segmenting natural images has been studied extensively, their robustness in segmenting FM images remains poorly understoodResultsTo address this deficiency, we have developed an assay that benchmarks robustness of DNN segmentation models using datasets of realistic synthetic 2D FM images with precisely controlled corruptions or adversarial attacks. Using this assay, we have benchmarked robustness of ten representative models such as DeepLab and Vision Transformer. We find that models with good robustness on natural images may perform poorly on FM images. We also find new robustness properties of DNN models and new connections between their corruption robustness and adversarial robustness. To further assess the robustness of the selected models, we have also benchmarked them on real microscopy images of different modalities without using simulated degradation. The results are consistent with those obtained on the realistic synthetic images, confirming the fidelity and reliability of our image synthesis method as well as the effectiveness of our assay.ConclusionsBased on comprehensive benchmarking experiments, we have found distinct robustness properties of deep neural networks in semantic segmentation of FM images. Based on the findings, we have made specific recommendations on selection and design of robust models for FM image segmentation.

Synthesis of multi-stimuli-responsive flocculants using reversible addition-fragmentation chain transfer polymerization: Investigating the performance and mechanism of kaolin coagulation

Coumarin-containing amphiphilic block copolymers with different compositions, as a new class of pH- and photo-responsive flocculants, were synthesized through reversible addition-fragmentation chain transfer polymerization of dimethyl aminoethyl methacrylate (DMAEMA), methyl methacrylate, and 7-acryloyloxy 4-methylcoumarin. Proton nuclear magnetic resonance spectroscopy was used to study the structure and composition of the copolymers. Zeta potential results show that the copolymers are applicable as amphoteric flocculants. The optimal flocculant concentration was calculated using the Jar analysis for kaolin flocculation, and the results show that the flocculation dependence on the flocculent concentration decreases with increasing its molecular weight. The dominance of charge neutralization in the flocculants with lower molecular weights and the dominance of adsorption bridging and hydrophobic association in the flocculants with higher molecular weights were indicated. These results are in agreement with the rheological data. To investigate the governing mechanism more precisely, the flocculation rate was calculated using turbidity measurements at different pH values over time. The highest final flocculation rate (99.25 %) corresponds to pH 4.5, which caused by the protonation of the amine groups of the DMAEMA repeating units. The dependence of settling rate on pH indicates the pH-dependent performance of the flocculants. Finally, fluorescence microscopy image of the flocculants shows its proper functioning of in the vicinity of kaolin, which has a promising industrial application.

Bayesian image segmentation under varying blur with triplet Markov random field

Abstract In this paper, we place ourselves in the context of the Bayesian framework for image segmentation in the presence of varying blur. The proposed approach is based on Triplet Markov Random Fields (TMRF). This method takes into account, during segmentation, peculiarities of an image such as noise, blur, and texture. We present an unsupervised TMRF method, which jointly deals with the problem of segmentation, and that of depth estimation in order to process fluorescence microscopy images. In addition to the estimation of the depth maps using the Metropolis-Hasting and the Stochastic Parameter Estimation (SPE) algorithms, we also estimate the model parameters using the SPE algorithm. We compare our TMRF method to other MRF models on simulated images, and to an unsupervised method from the state of art on real fluorescence microscopy images. Our method offers improved results, especially when blur is important.