Imaging Platform Research Articles

A modular platform for bioluminescent RNA tracking

AbstractA complete understanding of RNA biology requires methods for tracking transcripts in vivo. Common strategies rely on fluorogenic probes that are limited in sensitivity, dynamic range, and depth of interrogation, owing to their need for excitation light and tissue autofluorescence. To overcome these challenges, we report a bioluminescent platform for serial imaging of RNAs. The RNA tags are engineered to recruit light-emitting luciferase fragments (termed RNA lanterns) upon transcription. Robust photon production is observed for RNA targets both in cells and in live animals. Importantly, only a single copy of the tag is necessary for sensitive detection, in sharp contrast to fluorescent platforms requiring multiple repeats. Overall, this work provides a foundational platform for visualizing RNA dynamics from the micro to the macro scale.

DDDR-19. PHARMACOSCOPY FOR BRAIN METASTASES FROM NON-SMALL CELL LUNG CANCER (NSCLC)

Abstract Brain metastases remain a common and important complication in the course of disease in patients with non-small cell lung cancer (NSCLC). While targeted therapies and immunotherapies have led to prolonged disease control in subsets of patients with brain metastases from NSCLC, there is still an urgent need for novel treatments after failure of these standards and after exhaustion of local therapies including surgery and radiotherapy. Here we performed an explorative feasibility study to profile ex vivo drug sensitivities across a large panel of classical anti-cancer as well as neuroactive drugs, using an automated imaging platform called “pharmacoscopy”. The anti-cancer drug panel included chemotherapies and targeted therapies such as receptor tyrosine kinase, cyclin dependent kinase, and BCL2 inhibitors. The neuroactive drug panel included antidepressants and antipsychotics. A total of 19 samples from 17 patients were dissociated after surgery and incubated with drugs for 48 hours ex vivo to measure drug sensitivities. Patient cancer cells were identified by immunofluorescence using EpCAM, TTF-1, or CK7 as markers whereas immune cells were identified by CD45. On-target drug activity (pharmacoscopy score) was measured by quantifying the specific reduction of cancer cells upon drug treatment. Among the top ranking drugs ex vivo, several patient samples displayed on-target responses to candidate targeted therapies such as the CDK4/6 inhibitor abemaciclib, the BCL2 inhibitor venetoclax, and the MEK inhibitor cobimetinib as well as unexpected sensitivies to certain neuroactive drugs. Integration of pharmacoscopy results with molecular data from next generation sequencing in the respective brain metastases tissues is ongoing. Overall, this study demonstrates the feasibility of pharmacoscopy-based functional drug testing in NSCLC brain metastases, and highlights novel opportunities of personalized treatment for future exploratory clinical studies in patients with brain metastases from NSCLC.

Evaluation of a Novel MET-Targeting Camelid-Derived Antibody in Head and Neck Cancer.

In head and neck squamous cell carcinoma (HNSCC), the mesenchymal epithelial transition (MET) receptor drives cancer growth, proliferation, and metastasis. MET is known to be overexpressed in HNSCC and, therefore, is an appealing therapeutic target. In this study, we evaluated MET expression in patients with HNSCC and investigated the potential imaging application of a novel MET-binding single-domain camelid antibody using positron emission tomography/computed tomography (PET/CT) in a preclinical MET-expressing HNSCC model. Multiplex immunostaining for MET protein performed on a tissue microarray from 203 patients with HNSCC found 86% of patients to have MET expression, with 14% having high expression and 53% having low MET expression. Using The Cancer Genome Atlas (TCGA) database, high MET RNA expression was associated with worse progression-free survival and overall survival in patients with HPV-negative HSNCC. Utilizing flow cytometry and immunofluorescence, our novel camelid antibody fused to a human IgG Fc chain (1E7-Fc) showed high binding affinity and specificity to high MET-expressing Detroit 562 cells but not to low MET-expressing HNSCC cells. The efficacy and biodistribution of [89Zr]Zr-1E7-Fc as a PET imaging agent was then investigated in a MET-expressing head and neck xenograft model. [89Zr]Zr-1E7-Fc rapidly localized and showed high tumor uptake in Detroit 562 xenografts (8.4% ID/g at 72 h postinjection), with rapid clearance from the circulatory system (2.7 tumor-to-blood radioactivity ratio at 72 h postinjection). Our preclinical data suggest that the camelid antibody 1E7-Fc could be a potential theranostic agent for HNSCC. Further investigations are warranted to confirm these findings in patients and to evaluate 1E7-Fc as an imaging agent and platform to deliver radionuclide or drug therapy to MET-driven cancers.

Lensless On-Chip Chemiluminescence Imaging for High-Throughput Single-Cell Heterogeneity Analysis.

High-throughput single-cell heterogeneity imaging and analysis is essential for understanding complex biological systems and for advancing personalized precision disease diagnosis and treatment. Here, we present a miniaturized lensless chemiluminescence chip for high-throughput single-cell functional imaging with subcellular resolution. With the sensitive chemiluminescence sensing and wide field of view of contact lensless imaging, we demonstrated the chemiluminescent imaging of over 1000 single cells, and their membrane glycoprotein and the high-throughput single-cell heterogeneity of membrane protein imaging were examined for precision analysis. Furthermore, the functional adhesion and heterogeneity of single live cells were imaged and explored. This miniaturized lensless on-chip CL-CMOS imaging platform enables high-throughput single-cell imaging and analysis with high sensitivity and subcellular resolution, providing new techniques for the cellular study of biological heterogeneity and has potential application in precision disease diagnosis and treatment at the point-of-care settings.

Rapid-response RNA-fluorescence in situ hybridization (FISH) assay platform for coronavirus antiviral high-throughput screening

Over the past 25 years, the global community has faced challenges posed by three distinct outbreaks of coronaviruses including severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The identification of a novel alphacoronavirus canine CoV (CCoV-HuPn2018) in human patients in Malaysia underscores the potential for crossover infections to humans. The threat of the ever-evolving nature of viral infections as well as the lingering health and socioeconomic effects of the recent SARS-CoV-2 pandemic emphasize the urgent need for advanced antiviral drug screening tools that can be quickly implemented to strengthen preparedness and preventive measures against future outbreaks. Here, we present the development and validation of a novel RNA-fluorescence in situ hybridization (FISH) imaging assay as a 384-well, high-throughput rapid response platform for antiviral drug discovery. RNA-FISH is a powerful tool to visualize specific mRNA in cultured cells using a high-content imaging platform. The flexibility of RNA-FISH probe sets allows for the rapid design of viral genome-specific probes, enabling in vitro assay development to test for inhibition of viral replication by either biologic or small molecule inhibitors. Screening of 170 antiviral compounds in concentration-response demonstrates a strong correlation between the RNA-FISH assay and an immunofluorescence assay (IFA) for both human coronaviruses HCoV-OC43 and HCoV-229E. Additionally, we successfully applied this methodology in the context of CCoV strain 1–71, proving rapid development and deployment, opening new avenues for the evaluation of antiviral drugs to potential future emerging threats.

An integrated portable system for laser speckle contrast imaging and digital holographic microscopy

A multimodal quantitative phase imaging platform combining digital holographic microscopy (DHM) with laser speckle contrast imaging (LSCI) is demonstrated for imaging weakly scattering and transparent samples in a label-free manner. It uses the principle of coherence of the light source for interferometric detection of phase of transparent and semi-transparent samples. The speckle formation resulting from the coherence property of the laser source is used to track dynamic activities in the regions of interest in the sample. Integration of these two techniques onto a microfluidic chip leads to an optofluidic real-time microscope for live cell imaging applications. In this work, we have developed an integrated multimodal system combining digital holographic microscopy and laser speckle contrast imaging system for Optofluidics and in vitro studies. The sample flowing through the microfluidic channel is imaged to record holograms and video of intensity images at the same time. This enables to map the flow within a microfluidic channel and quantify the channel as well as the particle flow through the channel. The channel morphology along with the particles flowing through the channel are quantified using DHM. The two-dimensional speckle contrast images map the flow of the dynamic microbeads and cells with very high contrast in almost real time. A low-cost portable multimodal quantitative phase microscope combining DHM and LSCI has been demonstrated for real time imaging with applications in optofluidics.

LabEmbryoCam: An opensource phenotyping system for developing aquatic animals

Phenomics is the acquisition of high-dimensional data on an individual-wide scale and is proving transformational in areas of biological research related to human health including medicine and the crop sciences. However, more broadly, a lack of accessible transferrable technologies and research approaches is significantly hindering the uptake of phenomics, in contrast to molecular-omics for which transferrable technologies have been a significant enabler. Aquatic embryos are natural models for phenomics, due to their small size, taxonomic diversity, ecological relevance, and high levels of temporal, spatial and functional change. Here, we present LabEmbryoCam, an autonomous phenotyping platform for timelapse imaging of developing aquatic embryos cultured in a multiwell plate format, and while optimised for embryos, the instrument is extremely versatile. The LabEmbryoCam capitalises on 3D printing, single board computers, consumer electronics and stepper motor enabled motion. These provide autonomous X, Y and Z motion, a web application streamlined for rapid setup of experiments, user email notifications and a humidification chamber to reduce evaporation over prolonged acquisitions. Downstream analyses are provided, enabling automated embryo segmentation, heartbeat detection, motion tracking, and energy proxy trait (EPT) measurement. LabEmbryoCam is a scalable, and flexible laboratory instrument, that leverages embryonic and early life stage organisms to tackle key global challenges including biological sensitivity assessment, toxicological screening, but also to support broader engagement with the earliest stages of life.

High-Resolution In Situ Ultrasonic Imaging Platform for Studying Localized Corrosion Morphology

High-Resolution In Situ Ultrasonic Imaging Platform for Studying Localized Corrosion Morphology

Two-Step Iterative Medical Microwave Tomography.

In the field of medical imaging, microwave tomography (MWT) is based on the scattering and absorption characteristics of different tissues to microwaves and can reconstruct the electromagnetic property distribution of biological tissues non-invasively and without ionizing radiation. However, due to the inherently nonlinear and ill-posed characteristics of MWT calculations, actual imaging is prone to overfitting or artifacts. To address this, this paper proposes a two-step iterative imaging approach for rapid medical microwave tomography. This method establishes corresponding objective functions for microwave imaging across multiple frequencies and conducts iterative calculations on images at varying resolutions. This effectively enhances image clarity and accuracy while alleviating the issue of prolonged computational time associated with imaging complex structures at high resolution due to insufficient prior information during iterative processes. In the electromagnetic simulation section, we simulated a three-layer brain model and conducted imaging experiments. The results demonstrate that the algorithm significantly enhances imaging resolution, accurately pinpointing cerebral hemorrhages at different locations using an eight-antenna array and successfully reconstructs tomography images with a hemorrhage area radius of 1 cm. Lastly, experiments were conducted using a medical microwave tomography platform and four simplified human brain models, achieving millimeter-level accuracy in MWT.

Keratoconus.

Keratoconus is a progressive eye disorder primarily affecting individuals in adolescence and early adulthood. The ectatic changes in the cornea cause thinning and cone-like steepening leading to irregular astigmatism and reduced vision. Keratoconus is a complex disorder with a multifaceted aetiology and pathogenesis, including genetic, environmental, biomechanical and cellular factors. Environmental factors, such as eye rubbing, UV light exposure and contact lens wearing, are associated with disease progression. On the cellular level, a complex interplay of hormonal changes, alterations in enzymatic activity that modify extracellular membrane stiffness, and changes in biochemical and biomechanical signalling pathways disrupt collagen cross-linking within the stroma, contributing to structural integrity loss and distortion of normal corneal anatomy. Clinically, keratoconus is diagnosed through clinical examination and corneal imaging. Advanced imaging platforms have improved the detection of keratoconus, facilitating early diagnosis and monitoring of disease progression. Treatment strategies for keratoconus are tailored to disease severity and progression. In early stages, vision correction with glasses or soft contact lenses may suffice. As the condition advances, rigid gas-permeable contact lenses or scleral lenses are prescribed. Corneal cross-linking has emerged as a pivotal treatment aimed at halting the progression of corneal ectasia. In patients with keratoconus with scarring or contact lens intolerance, surgical interventions are performed.

Precision Phenotyping in Crop Science: From Plant Traits to Gene Discovery for Climate‐Smart Agriculture

ABSTRACTThe global population is placing unprecedented demand on food systems, which can be met only through a complex interplay of technology, sustainable food production intensification methods and climate resilience. To address such compounded requirements, developing high‐yielding crop varieties using precise plant breeding methods bolstered with efficient and nondestructive plant trait documentation approaches is vital. High‐throughput crop phenotyping (HTCP) platforms have prominently emerged as a mainstream approach for reducing the phenotyping bottleneck in breeding programmes. HTCP has the potential to provide detailed quantitative information of large plant populations under different growth stages across diverse environmental regimes, facilitating accelerated plant breeding strategies. New imaging platforms also enable nondestructive characterization of a wide range of above and below‐ground crop parameters. The specificity in use of sensors, automation of data collection, large‐scale data handling systems and accurate analytical tools have a substantial role in dynamic crop monitoring and big data interpretation. HTCP platforms are capable of making precise measurements of a wide range of physiological, morphological, biochemical and stress responses in plants. Developments of sensors with improved precision, intervention of unmanned aerial vehicles, robotics, computed tomography and machine learning have given a dramatic developmental leap to precise and large‐scale crop phenotyping. This review provides an avenue for understanding various high‐throughput phenotyping platforms, working principles, current developments and contributions to high‐throughput phenotyping of various crops under laboratory and field conditions. A detailed comparative idea on the advantages and pitfalls of these available platforms can help researchers in choosing the right technology suiting specific practical requirements. Furthermore, the review aims to provide novel future prospects and developmental requirements that can potentially widen the application and utilization of these HTCP technologies in agriculture.

An Omni-Mesoscope for multiscale high-throughput quantitative phase imaging of cellular dynamics and high-content molecular characterization.

The mesoscope has emerged as a powerful imaging tool in biomedical research, yet its high cost and low resolution have limited its broader application. Here, we introduce the Omni-Mesoscope, a high-spatial-temporal and multimodal mesoscopic imaging platform built from cost-efficient off-the-shelf components. This system uniquely merges the capabilities of label-free quantitative phase microscopy to capture live-cell morphodynamics across thousands of cells with highly multiplexed fluorescence imaging for comprehensive molecular characterization. This Omni-Mesoscope offers a mesoscale field of view of ~5 square millimeters with a high spatial resolution down to 700 nanometers, enabling the capture of detailed subcellular features. We demonstrate its capability in delineating molecular characteristics underlying rare morphodynamic cellular phenomena, including cancer cell responses to chemotherapy and the emergence of polyploidy in drug-resistant cells. We also integrate expansion technique to enhance three-dimensional volumetric super-resolution imaging of thicker tissues, opening the avenues for biological exploration at unprecedented scales and resolutions.

Cross-modality image translation of 3 Tesla Magnetic Resonance Imaging to 7 Tesla using Generative Adversarial Networks.

The rapid advancements in magnetic resonance imaging (MRI) technology have precipitated a new paradigm wherein cross-modality data translation across diverse imaging platforms, field strengths, and different sites is increasingly challenging. This issue is particularly accentuated when transitioning from 3 Tesla (3T) to 7 Tesla (7T) MRI systems. This study proposes a novel solution to these challenges using generative adversarial networks (GANs)-specifically, the CycleGAN architecture- to create synthetic 7T images from 3T data. Employing a dataset of 1112 and 490 unpaired 3T and 7T MR images, respectively, we trained a 2-dimensional (2D) CycleGAN model, evaluating its performance on a paired dataset of 22 participants scanned at 3T and 7T. Independent testing on 22 distinct participants affirmed the model's proficiency in accurately predicting various tissue types, encompassing cerebral spinal fluid, gray matter, and white matter. Our approach provides a reliable and efficient methodology for synthesizing 7T images, achieving a median Dice of 6.82%,7,63%, and 4.85% for Cerebral Spinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), respectively, in the testing dataset, thereby significantly aiding in harmonizing heterogeneous datasets. Furthermore, it delineates the potential of GANs in amplifying the contrast-to-noise ratio (CNR) from 3T, potentially enhancing the diagnostic capability of the images. While acknowledging the risk of model overfitting, our research underscores a promising progression towards harnessing the benefits of 7T MR systems in research investigations while preserving compatibility with existent 3T MR data. This work was previously presented at the ISMRM 2021 conference (Diniz, Helmet, Santini, Aizenstein, & Ibrahim, 2021).

Optimization of Pancreatic Cell Subset Classification in Images from Multiplex Immunofluorescence Imaging Platforms

Understanding the natural history of type 1 diabetes (T1D) has been hampered by limited access to the target organ (i.e., the pancreas) in living people. To make the most of these precious tissues, cyclic imaging platforms such as Miltenyi MACSima, which increase the number of markers that can be examined per section, are becoming increasingly popular. Such advances in spatial biology require reproducible cell classification algorithms. Random Tree (RT) algorithm was previously found to be the most reliable approach for differentiating exocrine versus endocrine pancreas compartments, yet an unmet need exists for an approach that can provide accurate cell type classification within the endocrine compartment. It was hypothesized that a core set of markers could be used to maximize classification accuracy. To this end, pancreatic sections were stained with fluorescent antibodies against various markers and images were captured using the MACSima platform. RT algorithm was trained in QuPath to classify endocrine, exocrine, and vascular cell clusters in n=3 pancreas donors across a total of n=10 ROIs. Classification accuracy will be determined by comparison to ground-truth labels annotated by an expert in the field. Markers that maximize classification accuracy will be included on future pancreas runs to standardize and streamline downstream analyses.

ESPressoscope: A small and powerful approach for in situ microscopy.

Microscopy is essential for detecting, identifying, analyzing, and measuring small objects. Access to modern microscopy equipment is crucial for scientific research, especially in the biomedical and analytical sciences. However, the high cost of equipment, limited availability of parts, and challenges associated with transporting equipment often limit the accessibility and operational capabilities of these tools, particularly in field sites and other remote or resource-limited settings. Thus, there is a need for affordable and accessible alternatives to traditional microscopy systems. We address this challenge by investigating the feasibility of using a simple microcontroller board not only as a portable and field-ready digital microscope, but furthermore as a versatile platform which can easily be adapted to a variety of imaging applications. By adding a few external components, we demonstrate that a low-cost ESP32 camera board can be used to build an autonomous in situ platform for digital time-lapse imaging of cells. Our prototype of this approach, which we call ESPressoscope, can be adapted to applications ranging from monitoring incubator cell cultures in the lab to observing ecological phenomena in the sea, and it can be adapted for other techniques such as microfluidics or spectrophotometry. Our prototype of the ESPressoscope concept achieves a low power consumption and small size, which makes it ideal for field research in environments and applications where microscopy was previously infeasible. Its Wi-Fi connectivity enables integration with external image processing and storage systems, including on cloud platforms when internet access is available. Finally, we present several web browser-based tools to help users operate and manage our prototype's software. Our findings demonstrate the potential for low-cost, portable microscopy solutions to enable new and more accessible experiments for biological and analytical applications.

Quantitative Analysis of Cell-Free RNA at Attomolar Level Using CRISPR/Cas Digital Imaging Platform.

Quantitative analysis of cell-free RNA (cfRNA) in plasma sample can be used for screening, diagnosing, and prognosticating of multiple diseases. Here, we report a quantitative CRISPR/Cas digital imaging platform (qCasdip) for the detection of various cfRNAs, including circular RNAs and miRNAs, in clinical samples at the attomolar (aM) level without the need for preamplification. Digital counting strategy provides qCasdip quantitative ability with a linear detection range of 102-106 aM. Meanwhile, qCasdip demonstrated cfRNA profiling in clinical plasma samples, improving the diagnosis of breast cancer. These data highlight the potential of qCasdip to quantitatively assess the molecular patterns of specific cfRNA panels in plasma, thereby providing a novel liquid biopsy solution to enhance disease diagnosis.

Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis.

Recent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance our understanding of treatment response and disease progression. Cellular neighborhood analysis is a popular computational approach for these complex image data. Despite its popularity, there are significant challenges, including high computational demands that limit feasibility for large-scale applications and the lack of a principled strategy for integrative analysis across images. This absence hampers the precise and consistent identification of spatial features and tracking of their dynamics over disease progression. To overcome these challenges, we introduce SpaTopic, a spatial topic model designed to decode high-level spatial architecture across multiplexed tissue images. This algorithm integrates both cell type and spatial information within a topic modelling framework, originally developed for natural language processing and adapted for computer vision. Spatial information is incorporated into the flexible design of documents, representing densely overlapping regions in images. The model employs an efficient collapsed Gibbs sampling algorithm for both statistical and computational inference. We benchmarked the performance against five state-of-the-art algorithms through various case studies using different single-cell spatial transcriptomic and proteomic imaging platforms across different tissue types. Our findings demonstrate that SpaTopic consistently identifies biologically and clinically significant spatial "topics" such as tertiary lymphoid structures (TLSs) and tracks dynamic changes in spatial features over disease progression. Its computational efficiency and broad applicability across various molecular imaging platforms will enhance the analysis of large-scale tissue imaging datasets.

Study on the Electrochemiluminescence Emission Mechanism of HOF-14 and Its Multimode Sensing and Imaging Application.

A novel hydrogen-bonded organic framework (HOF-14) has attracted much attention due to its excellent biocompatibility and low toxicity, but its research in the electrochemiluminescence (ECL) field has not been reported. In this work, the annihilation-type and coreactant-type ECL emission mechanisms of HOF-14 were studied systematically for the first time. It was found that the ECL quantum efficiency of HOF-14/TEA coreactant system was the highest, which was 1.82 times that of Ru(bpy)32+/TEA. Further, the ECL emission intensity of HOF-14/TEA system could achieve colorimetric (CL) imaging of mobile phone. We also discovered that HOF-14 had superior photoelectrochemical (PEC) performance. Based on the above research results, a unique HOF-14-based multimode sensing and imaging platform was constructed. The antibiotic Enrofloxacin (ENR) was selected as the detection target, and the Cu-Zn bimetallic single-atom nanozyme (Cu-Zn/SAzyme) with excellent peroxidase (POD)-like activity was used to prepare quenching probes. When the target ENR was present, Cu-Zn/SAzyme quenching probes were introduced to the surface of HOF-14 by the dual-aptamer sandwich method. Cu-Zn/SAzyme could catalyze diaminobenzidine (DAB) to produce brown precipitations to quench the ECL, PEC, and CL signals of HOF-14, realizing multimode detection of ENR. This work not only discovered excellent ECL and PEC property of new HOF-14 material but also systematically studied the ECL emission mechanism of HOF-14, and proposed a novel multimode sensing and imaging platform, which greatly improved the detection accuracy of target and showed great contributions to the field of ECL analysis.

New Approaches to Herbicide and Bioherbicide Discovery

Abstract During the past 30 yr an impasse has developed in the discovery and commercialization of synthetic herbicides with new molecular targets and novel chemistries. Similarly, there has been little success with bioherbicides, both microbial and chemical. These bioherbicides are needed to combat fast-growing herbicide resistance and to fulfill the need for more environmentally and toxicologically safe herbicides. In response to this substantial and growing opportunity, numerous start-up companies are utilizing novel approaches to provide new tools for weed management. These diverse new tools broaden the scope of discovery, encompassing advanced computational, bioinformatic, and imaging platforms; plant genome–editing and targeted protein degradation technologies; and machine learning and artificial intelligence (AI)-based strategies. This review contains summaries of the presentations of 10 such companies that took part in a symposium held at the WSSA annual meeting in 2024. Four of the companies are developing microbial bioherbicides or natural product–based herbicides, and the other six are using advanced technologies, such as AI, to accelerate the discovery of herbicides with novel molecular target sites or to develop non-GMO, herbicide-resistant crops.

High performance self-driven broadband photodetector for polarized imaging based on novel ZrS3/ReSe2 van der Waals heterojunction

The distinctive characteristics of anisotropic two-dimensional (2D) materials, including in-plane anisotropy of optical absorption and carrier mobility, render them exceptionally suitable for application in the field of polarization detection and as a novel platform for the polarization imaging. Meanwhile, the consolidation of diverse functionalities within a single photodetector is highly anticipated to meet the demands of some special scenarios. Herein, a novel ZrS3/ReSe2 van der Waals (vdWs) heterostructure device was successfully constructed to realize polarization-sensitive, self-powered, and broadband photodetection and imaging. Owing to the built-in electric field of the type-II band alignment within the heterojunction, the device achieves a self-powered photoresponse ranging from 300 to 980 nm, an ultralow dark currentt ∼1 pA, and a commendable rise/decay time of 0.35/0.28 ms. Additionally, it has been demonstrated that the self-driven photodetector possesses a polarization-sensitivity with a notable anisotropic ratio about 2.02 (1.98) under 490 nm (980 nm) light illumination with zero bias, coupled with an excellent repeatability and stability. Furthermore, we also demonstrate the polarization imaging capabilities of the device in visible and near-infrared spectrum, realizing a contrast-enhanced degree of linear polarization imaging. This work paves a new platform to develop heterojunction photodetectors for high performance polarization-sensitive photodetection and next-generation polarized imaging.