Spatial Measurements Research Articles

Unsupervised Image Segmentation Parameters Evaluation for Urban Land Use/Land Cover Applications

Image segmentation plays an important role in object-based classification. An optimal image segmentation should result in objects being internally homogeneous and, at the same time, distinct from one another. Strategies that assess the quality of image segmentation through intra- and inter-segment homogeneity metrics cannot always predict possible under- and over-segmentations of the image. Although the segmentation scale parameter determines the size of the image segments, it cannot synchronously guarantee that the produced image segments are internally homogeneous and spatially distinct from their neighbors. The majority of image segmentation assessment methods largely rely on a spatial autocorrelation measure that makes the global objective function fluctuate irregularly, resulting in the image variance increasing drastically toward the end of the segmentation. This paper relied on a series of image segmentations to test a more stable image variance measure based on the standard deviation model as well as a more robust hybrid spatial autocorrelation measure based on the current Moran’s index and the spatial autocorrelation coefficient models. The results show that there is a positive and inversely proportional correlation between the inter-segment heterogeneity and the intra-segment homogeneity since the global heterogeneity measure increases with a decrease in the image variance measure. It was also found that medium-scale parameters produced better quality image segments when used with small color weights, while large-scale parameters produced good quality segments when used with large color factor weights. Moreover, with optimal segmentation parameters, the image autocorrelation measure stabilizes and follows a near horizontal fluctuation while the image variance drops to values very close to zero, preventing the heterogeneity function from fluctuating irregularly towards the end of the image segmentation process.

Contribution of VLF electromagnetic survey to the investigation of Hessdalen lights (Norway)

Hessdalen valley in Norway is known for luminous phenomena suddenly and evenly appearing temporarily. Since several decades, these phenomena are observed by many witnesses, and they are sometimes traced by geophysical devices. The first appearance in modern times was reported in 1981 but systematic observations started during winter of 1984 when the Hessdalen project was launched. Later, Østfold University College led the project and yearly organized one to two field campaigns, with the objective to systematically record and investigate the phenomenon. Till that epoch, detailed tectonics, fault systems and superficial conductive structures remained unknown. Therefore, during the last years, VLF surveys have been performed in Hessdalen valley as part of six geophysical field campaigns which sometimes also included Total Magnetic Field and Self Potential spatial ground measurements. VLF measurements have been carried out on a 20 m average spacing along many traces totaling to100 km length. The entire covered area was about 100 km2.In this paper, we focus on the results of the VLF measurements. Several conductive zones have been found. They are mainly related to mineral deposits (mainly sulfides). The trace at the ground surface of these conductive zones could suggest that they draw an ellipse of 6 by12 km, related to the shape of the gabbro intrusion present in the area and oriented in the SW-NE direction. The results combined with other geophysical data contribute to better understand how the near surface structures (depth less than a few hundred meters) could supply the generation of the so-called Hessdalen lights (‘HL’) and explain why these lights appear inside this valley. The particular geological structure detected in Hessdalen valley may encourage similar campaigns in other areas where similar phenomena are observed.

The spatial resolution measurements on the small prototype of the Super Charm-Tau Factory drift chamber

Budker Institute of Nuclear Physics is actively evolving the Super Charm-Tau Factory project. A drift chamber with a small hexagonal cell was proposed for this collider. For experimental measurements of a spatial resolution, a drift chamber small prototype consisting of seven cells has been developed. The experiments have been carried out in He/C3H8 (60/40) and He/C2H6 (50/50) gas mixtures at different gas gains using cosmic particles. For this purpose, a calibration procedure for R(t, α) has been developed and fine-tuned, taking into account small prototype features and an angular dependence of isochrones at the edge of the cells. As a result, the average spatial resolutions are less than 100μm for gas gains greater than 5⋅104.

Integration of LST and NDVI for adjusted night light index (ILNANLI): a novel index to reduce the effect of saturation and blooming of DMSP-OLS data

ABSTRACT DMSP-OLS night-light images have provided an effective way to monitor cities on a global scale. These images can separate urban areas and other human activities from the background without light with correct spatial measurement. However, due to saturation and blooming problems, the ability of these images to describe urban features is limited, especially in urban cores. Therefore, in this research, a new index, ILNANLI, has been developed and presented by integrating LST and NDVI for the adjusted night-light index. This index has two general goals: reducing the effects of saturation in the city centres, increasing the variety of brightness pixel values of night-light images, and reducing the blooming in the surrounding areas. The ILNANLI has been developed in such a way that it has put the indicators and relationships together, taking into account the characteristics of different regions of the city (such as buildings, vegetation, and soil) in such a way that in every urban environment (temperate and dry and semi-desert) to be successful. Another feature is its easy implementation and high success. In fact, the prominent feature of this index, which distinguishes it from the rest of the indices, is that it has been able to work successfully in arid and semi-desert areas in addition to temperate regions. The evaluation results showed that the average overall accuracy of ILNANLI in mapping and extracting built-up areas with automatic thresholds in cities with different climates has effectively increased by 8% compared to other indexes. Also, the examination of transects shows that the proposed index is more effective than the other four indexes in increasing the diversity of values and reducing blooming. Therefore, as a favourable option with easy implementation, the proposed index can be used to reduce the effect of saturation and blooming and study the urban structure at local scales.

Measuring human perception of residential built environment through street view image and deep learning

As an important part of the urban built environment, streets exploring the influence mechanism between the built environment and human perception. It is one of the issues in building healthy cities. In this study, the residential streets of Zhongshan Distict, Dalian were selected as the study site, including Mountain Low-rise Neighborhood, Old Mid-rise Neighborhood, and Modern High-rise Neighborhood. Meanwhile, spatial measurement and human perception perception evaluation of the street environment were based on Deep learning and street view image (SVI). The study used human perceptions as dependent variables, and physical features as the independent variables. Finally, two regression models of positive and negative perceptions were established to analyze the relationship between them. The results showed that in the three types of neighborhood, positive perception was mainly focused on Mountain Low-rise Neighborhood; Negative perception was mainly focused on Old Mid-rise Neighborhood. Greenness, Openness, Natural Landscape, Natural to artificial ratio of the horizontal interface, and Natural to artificial ratio of the vertical interface had a positive influence on positive perception. Pedestrian occurrence rate, Enclosure, and Vehicle Occurrence rate had a negative influence on negative emotive. Greenness was the physical feature that most affected human perception. This study provided a method for objectively evaluating the quality of the street built environment. It is important for promoting the quality of residential streets and public mental health.

Quantitative Analysis of Phagocytosis in Whole Blood Using Double Staining and Visualization.

Phagocytosis is an essential innate immunity function in humans and animals. A decrease in the ability to phagocytize is associated with many diseases and aging of the immune system. Assessment of phagocytosis dynamics requires quantification of bacteria inside and outside the phagocyte. Although flow cytometry is the most common method for assessing phagocytosis, it does not include visualization and direct quantification of location of bacteria. Here, we used double-labeled Escherichia coli cells to evaluate phagocytosis by flow cytometry (cell sorting) and confocal microscopy, as well as employed image cytometry to provide high-throughput quantitative and spatial recognition of the double-labeled E. coli associated with the phagocytes. Retention of pathogens on the surface of myeloid and lymphoid cells without their internalization was suggested to be an auxiliary function of innate immunity in the fight against infections. The developed method of bacterial labeling significantly increased the accuracy of spatial and quantitative measurement of phagocytosis in whole blood and can be recommended as a tool for phagocytosis assessment by image cytometry.

Spatial Accessibility Measures for Egyptian Cities' Urban Parks Utilizing GIS-Based Methods

Spatial Accessibility Measures for Egyptian Cities' Urban Parks Utilizing GIS-Based Methods

Large-scale flow field super-resolution via local-global fusion convolutional neural networks

Particle image velocimetry (PIV) techniques have a limited field of view of the flow field and can only capture high-resolution flow fields in localized areas. To obtain a larger measurement range, multiple cameras must be used to capture the flow field simultaneously and then stitch the parts together. However, this method can be expensive. We propose the local-global fusion convolutional neural network (LGF-CNN) for reconstructing large-field flow fields with high spatial resolution based only on two flow data types: local small-field high spatial resolution wake velocity fields and global large-field low spatial resolution velocity fields. The core of the model consists of convolutional neural network (CNN) architecture to learn the mapping relationship between the small field of view with high spatial resolution and the large field of view with low spatial resolution. Using the effectively trained LGF-CNN model, we demonstrate its ability to reconstruct high-resolution velocity fields around the circular cylinder. The LGF-CNN is rigorously validated on a number of representative datasets, including simulated data for Reynolds numbers of 200 and 500, as well as experimental data for a Reynolds number of 3.3 × 104 with the steady jet at the rear stagnation point of the cylinder. The results demonstrate the ability of LGF-CNN to generate accurate velocity fields with high spatial resolution, including reliable detection of high-frequency components. The proposed method could reduce the number of cameras required for large-field, high spatial resolution PIV measurements, thereby reducing experimental costs.

Studies on a hadronic calorimeter with MPGD technology for a future Muon Collider experiment

In the context of the European strategy for particle physics, the Multi-TeV Muon Collider has emerged as a compelling alternative for advancing our understanding of the Standard Model, after the full exploitation of the High-Luminosity LHC. The physics programme at the Muon Collider includes precise measurements in the Higgs boson sector and the search for new physics at the TeV scale. Achieving these goals relies on accurate full event reconstruction, including the identification and precise four-momentum estimation of various particles. The Particle Flow (PF) algorithm is one of the most suitable approach for this task, exploiting information from tracking, calorimeter, and muon detectors for particle identification and measurements of their momenta/energies. Tracking detectors measure charged particle momenta, while calorimeters provide energy measurements for photons and neutral hadrons. Therefore, a combination of an exceptional tracking system and high-granularity calorimeters is necessary. However, one of the biggest challenges for a future experiment at the Muon Collider is to discriminate the product of the μμ collisions from the intense beam-induced-background (BIB), due to the unstable nature of muons, whose decay products interact with the detector material. To address this, an innovative hadronic calorimeter (HCAL) based on Micro Pattern Gas Detectors (MPGDs) is proposed. MPGDs offer robust technology for high radiation environments and a high granularity for precise spatial measurements. Dedicated studies are needed to assess and optimize the performance of an MPGD-based HCAL, including the development of medium-scale prototypes for performance measurements. The response of HCAL to incoming particles is examined through Monte Carlo simulations using Geant4, comparing the performance of digital and semi-digital readouts, with energy resolution as the figure of merit. The simulated geometry will be integrated into the Muon Collider software to study its impact on jet reconstruction within the full apparatus and in the presence of BIB. The simulation will be also validated through the test of a small-size calorimeter cell equipped with advanced resistive MPGD technologies, namely resistive MicroMegas, resistive μRWELL and RP-WELL.

How does rapid urban construction land expansion affect the spatial inequalities of ecosystem health in China? Evidence from the country, economic regions and urban agglomerations

Identifying the adaptation mechanisms between urban construction land expansion (UCE) and ecosystem health (EHI) is a crucial aspect of sustainable management. However, there is still progress room for existing studies in distinguishing the impact mechanisms of UCE on the EHI of China's cities, different economic subregions and urban agglomerations. These gaps hinder the development of differentiated supportive frameworks for urban land management policies and ecosystem restoration initiatives across various scales in China. This study integrated multivariate satellite remote sensing data to monitor the heterogeneous evolution patterns of EHI and UCE in Chinese cities, economic subregions and urban agglomerations. A set of spatial measure models are utilized to distinguish the variability impact of UCE on China's EHI from a multi-dimensional perspective, including global and local, whole-area and sub-area, and direct and indirect influences. The results indicate that as UCE continues to grow, EHI steadily declined, reaching 0.512 with an average annual decrease of 1.507% from 2000 to 2020. A diminishing cluster pattern of EHI was observed, primarily concentrated in urban agglomerations such as BTH, GBA, and YRD. UCE exhibited a significant negative spatial dependence on EHI, and both global and local perspectives, UCE had a gradually weakening negative effect on the EHI of Chinese cities and economic subregions. However, the impacts of UCE on EHI in mature urban agglomerations (e.g., GBA, BTH, and YRD) were dominated by spatial spillovers, whereas growing urban agglomerations (e.g., MRYRU and CCU) were more affected by direct shocks from UCE. Our investigation informs site-specific ecologically sustainable management strategies in China based on cross-scale analyses, providing new perspectives into establishing cooperative mechanisms for cross-regional ecological management while emphasizing ecological risks management under the integration of urban agglomerations.

Distance and T-square sampling for spatial measures of tree diversity

Distance sampling and its statistically improved variant, T-square sampling, are important sampling methods in plant ecology. They have often been applied in the context of plant density estimations and are comparatively easy to implement, since they intuitively follow the nearest-neighbour principle and thus do not require the layout of sample plots. Previous research studying distance sampling suggested that T-square sampling may also lead to an improved estimation of spatial tree diversity indices. We simulated distance and T-square sampling in six large fully mapped forest areas for seven tree diversity indices of which some competed for the same diversity aspect, i.e. tree location (dispersion), tree species and tree size diversity. Our results demonstrated that both distance and T-square sampling are indeed robust methods for sampling spatial measures of tree diversity. The sample size required for a sampling error of 10% does not exceed 20% of the total number of trees in a sampling area. T-square sampling has the ability to adapt to different spatial patterns of tree locations and this ability is key to the way the method controls estimation bias. The sample size required for species mingling and size differentiation clearly depends on the underlying spatial tree pattern in the sampling area. With most diversity indices, sample size reductions between 0.06% and 40% could be achieved by the application of T-square sampling compared to traditional distance sampling. All other conditions being equal, we could identify the uniform angle index, the species mingling index and the size differentiation index as those diversity indices achieving lower sampling error values than their competitors. For tree density estimations the Diggle and Byth estimators performed best. Based on our results, T-square sampling can be considered a robust sampling method for spatial tree diversity indices that is easy to apply in the field.

Improving spatial resolution of chlorophyll-a in the Mediterranean sea based on machine learning

The objective of this study is to increase the spatial resolution of data on the level of chlorophyll-a in the Mediterranean Sea using satellite images and ground measurements. The goal of this study is to develop an information technology based on machine learning to create chlorophyll-a concentration maps with high spatial resolution for the pilot areas of the Mediterranean Sea. Traditional ground-based methods for measuring chlorophyll-a are time-consuming, expensive, and have limited spatial and temporal coverage. Therefore, satellite observations have become an effective tool for monitoring chlorophyll-a over large areas. Low spatial resolution satellite data such as GCOM-C/SGLI and Sentinel-3 OLCI allow measurements of chlorophyll-a concentration at the sea surface. However, these data have limited accuracy and spatial resolution, which creates challenges for monitoring local changes in coastal zones and small water areas. Tasks: to analyze available satellite data and ground-based measurements of chlorophyll-a for the Mediterranean Sea; to investigate the correlation between satellite data of different spatial resolutions and ground measurements; to select informative features from satellite data for building machine learning models; and to develop models for increasing the spatial resolution of chlorophyll-a based on regression and machine learning algorithms. Obtained results: information technology combining satellite data with ground measurements in the Google Earth Engine cloud platform is proposed; correlations between satellite measurements of chlorophyll-a and ground data are investigated; models based on Random Forest and Multilayer Perceptron with coefficients of determination up to 0.36 and correlation of 0.6 with test data are built; chlorophyll-a maps with a spatial resolution of 10 m are created for the pilot area near Cyprus. Conclusions. The developed information technology allows the effective combination of satellite data of different spatial resolutions and ground measurements to increase the accuracy and detail of chlorophyll-a maps in the Mediterranean Sea. Further research involves improving the preprocessing of satellite data, using more features, involving data from other regions, and applying more sophisticated machine learning models.

Identifying multiple synergistic factors on the susceptibility to stress relaxation cracking in variously heat-treated weldments

The 347H austenitic stainless steel has been widely used for pressure vessels and pipeline (PVP) applications due to its excellent creep and corrosion resistance, which fit ideally to the harsh conditions in petrochemical industries, fossil fuel or nuclear power plants, and modern energy storages. However, a failure mode has been commonly observed with cracks emerging at the heat affected zone (HAZ) of weldments during post-weld heat treatment (PWHT) or under intermediate to high temperature service conditions. This phenomenon is termed as Stress Relaxation Cracking (SRC) since the purpose of PWHT is to relieve the welding-induced residual stress fields, or as Stress Age Cracking (SAC) if failure happens during service. A leading literature explanation of this failure suggests that the residual stress relaxation and the precipitation dissolution and/or re-precipitation occur in the same temperature range, which can lead to locally high strains and thus to crack at the grain boundaries. Since in situ spatial measurements of residual stress fields, microstructural evolution, and failure processes are nearly infeasible, this work recourses to a micromechanical finite element framework that models the high temperature failure as the nucleation and growth of grain boundary cavities, whereas various parameters such as thermomechanical loading history and its evolution, the competition of grain-interior dislocation creep and grain-boundary diffusion in failure lifetime, and microstructural heterogeneities (such as the precipitate free zone near grain boundaries) can be quantitatively incorporated. It can be concluded from these microstructure-explicit simulations that an accurate knowledge of residual stress evolution and a carefully calibrated set of material constitutive parameters are the essential prerequisites for lifetime predictions. The understanding of individual governing factors also leads to a mechanistic interpretation of the observed SRC susceptibility C-curves. These results suggest that the criticality of residual stress evolution, but not the precipitation-induced local strains, be the leading factor for SRC.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, ‘what you put is what you get’ (WYPIWYG) – that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

Tools and methods for high-throughput single-cell imaging with the mother machine.

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

Spatial error prediction method for precision machine tools considering the influence of repeatability error

A spatial error prediction method for precision machine tools considering the influence of repeatability errors is proposed in this paper. Firstly, the reciprocal of the repeatability error is used to evaluate the measurement data confidence to distinguish the difference of useful information contained in the measurement data of different positions. Secondly, the proposed confidence index is used to weight the measured values of PDGEs at different positions, so that the data containing more useful information can play a full role in the error prediction, and the predicted error is substituted into the error model to predict the spatial error. Then, the computer simulation based on statistical analysis is designed, the PDGEs and spatial error prediction accuracy of the weighted algorithm are verified by simulation. Finally, a multi-instrument fusion spatial error measurement scheme is proposed, and the accuracy and reliability of the model are verified on a horizontal machining center prototype.

On the data assimilation of initial distribution for 2-dimensionalshallow-water equation model

Abstract The utilization of data assimilation (DA) techniques is prevalent in marine meteorology for the purpose of estimating the complete state of the system. This is done to address the practical limitations associated with measurement data, which can only be specified at finite number of discrete points within a limited domain. We develop an efficient DA algorithm to reconstruct the initial state of the shallow-water equations (SWE) within a 2-dimensional rectangular domain using sparse spatial measurement data. Our algorithm takes into account both the complete Coriolis force and the ocean bottom topography in the SWE model, resulting in accurate recovery of the initial status. After establishing the uniqueness of the solution to the nonlinear SWE with appropriate boundary conditions, we proceed to establish the conservation laws for the suitably defined energy quantity for this traveling wave system. This generalization of the known conservation laws for the simplified SWE system, which ignores the Coriolis force and topography, allows us to reveal the influence of nonconstant sea floor topography on wave propagation. In order to restore the initial state through the minimization of a cost functional using DA techniques, we proceed by deriving the adjoint problem for our iteration process. Additionally, we establish a discrete scheme for the governing equations in the Arakawa C-grid framework, from which we rigorously derive the error associated with energy conservation in discrete form. The numerical implementations are also provided to validate our proposed scheme through the verification of energy conservation and the reconstruction effect of the initial state for various configurations.

Studying the Role of Visuospatial Attention in the Multi-Attribute Task Battery II

Task batteries mimicking user tasks are of high heuristic value. Supposedly, they measure individual human aptitude regarding the task in question. However, less is often known about the underlying mechanisms or functions that account for task performance in such complex batteries. This is also true of the Multi-Attribute Task Battery (MATB-II). The MATB-II is a computer display task. It aims to measure human control operations on a flight console. Using the MATB-II and a visual-search task measure of spatial attention, we tested if capture of spatial attention in a bottom-up or top-down way predicted performance in the MATB-II. This is important to understand for questions such as how to implement warning signals on visual displays in human–computer interaction and for what to practice during training of operating with such displays. To measure visuospatial attention, we used both classical task-performance measures (i.e., reaction times and accuracy) as well as novel unobtrusive real-time pupillometry. The latter was done as pupil size covaries with task demands. A large number of analyses showed that: (1) Top-down attention measured before and after the MATB-II was positively correlated. (2) Test-retest reliability was also given for bottom-up attention, but to a smaller degree. As expected, the two spatial attention measures were also negatively correlated with one another. However, (3) neither of the visuospatial attention measures was significantly correlated with overall MATB-II performance, nor with (4) any of the MATB-II subtask performance measures. The latter was true even if the subtask required visuospatial attention (as in the system monitoring task of the MATB-II). (5) Neither did pupillometry predict MATB-II performance, nor performance in any of the MATB-II’s subtasks. Yet, (6) pupil size discriminated between different stages of subtask performance in system monitoring. This finding indicated that temporal segregation of pupil size measures is necessary for their correct interpretation, and that caution is advised regarding average pupil-size measures of task demands across tasks and time points within tasks. Finally, we observed surprising effects of workload (or cognitive load) manipulation on MATB-II performance itself, namely, better performance under high- rather than low-workload conditions. The latter findings imply that the MATB-II itself poses a number of questions about its underlying rationale, besides allowing occasional usage in more applied research.

Robust high-order petal-mode laser with tunable topological charge pumped by an axicon-based annular beam

In this work, we demonstrate a watt-level laser producing high-order, petal-shaped output modes with tunable topological charge, by using an axicon-based divergent annular pump beam. The topological charge of the output beam could be varied in the range of 34–72 by adjusting the position of the Nd:YVO4 crystal relative to the focal plane of the pump beam. The highest order petal-mode beam generated from the system had a topological charge of 72 and a power of 1.3 W. The highest output power up to 2.1 W with a topological charge of 34 was achieved at an absorbed pump power of 5.8 W. The generated output modes were observed to be robust under power scaling and on propagation, with the same spatial profiles being maintained in the near- and far-fields. We anticipate that this system design may find use as a laser source in applications such as 3D optical trapping, fabrication of optical vortex arrays, optical communications, and high-sensitivity spatial measurement.

Abstract LB333: Improved spatially resolved single-cell transcriptomic imaging in archival tissues with MERSCOPE

Abstract The advent of spatial transcriptomics has enabled a revolution in how complex tissues are studied. However, samples with lower quality RNA due to degradation, protein crosslinking, or high RNase content remain challenging for spatial transcriptomic measurement. In particular, formalin fixed, paraffin embedded (FFPE) tissues are the most widely used sample types in clinical and molecular diagnosis, yet they are notoriously difficult for single-cell transcriptomic analysis. To accurately profile the gene expression in FFPE samples in situ, a spatial transcriptomics technique with high detection efficiency and single molecule resolution is required. The Vizgen® MERSCOPE® Platform for spatial genomics is built on Multiplexed Error Robust in situ Hybridization (MERFISH) technology and directly profiles the transcriptome of intact tissues with high sensitivity in high-quality samples. Here we present an updated workflow to perform MERFISH in low and high-quality samples. We demonstrated its application in more than 5 FFPE sample types from mouse and human, including archival samples. For each tissue type, hundreds of thousands of cells were captured using the updated MERSCOPE Platform workflow, generating 100s million counts and their spatial information for profiled genes in each sample. The updated workflow involves streamlined sample preparation and chemistry optimization to improve sensitivity. MERSCOPE accurately profiled gene expression in situ and mapped cell types in archival human samples across a range of low and high RNA qualities. We compared the performance of MERSCOPE imaging using the updated protocol to the previous version and observed a significant increase in gene counts per 100 micron2 of tissue. We also demonstrated increased reproducibility between replicates with the streamlined workflow and chemistry. Furthermore, we demonstrated the updated workflow is compatible with simultaneous protein-based cell boundary staining. Finally, we constructed a spatially resolved single-cell atlas across low-quality archival breast and lung tumor types, mapped and cataloged different cell types within the tumor microenvironment, and systematically characterized the gene expression among cells. Spatially resolved transcriptomic profiling of low-quality samples at single-cell level provides enormous opportunities in cancer research. These improvements will enable new genomic inquiries into previously intractable tissues like FFPE, leading to new biological insights into cancer progression. Citation Format: Jiang He, Bin Wang, Justin He, Renchao Chen, Benjamin Patterson, Sudhir Tattikota, Timothy Wiggin, Lizi Maziashvili, Peter Reinhold, Manisha Ray, George Emanuel. Improved spatially resolved single-cell transcriptomic imaging in archival tissues with MERSCOPE [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB333.