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In this thesis, I study the integrated and spatially resolved (kilo-parsec scale) properties of star-forming and quiescent galaxies across cosmic time to understand their evolution. First, using a sample of star-forming galaxies at intermediate redshifts (0.1 < z < 0.42) selected from the MUSE Wide Survey, I investigate the spatial distribution of H-alpha star formation rate (SFR) and the Balmer decrement using data from MUSE integral field spectrograph. For the same galaxies, I derive spatially resolved mass, SFR and dust maps from pixel-by-pixel Spectral Energy Distribution (SED) fitting on multiband photometric data from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) taken with the Hubble Space Telescope. I find strong dependence of the radial profiles of both stellar and nebular color excesses on the integrated specific SFR of star-forming galaxies. My results support the inside-out scenario for the growth of these galaxies. Next, I investigate the chemical abundances and star formation histories of high-redshift quiescent galaxies. My sample consists of five unique gravitationally lensed galaxies at 1.95 < z < 2.64 which includes MRG-M0138, the brightest lensed quiescent galaxy detected in the near infrared wavelengths. This is the only sample of quiescent galaxies at z ~ 2 for which we have a precise measure of stellar abundances as well as spatially resolved measurements, made possible due to strong gravitational lensing. I first analyze spectra of MRG-M0138 to measure magnesium-enhancement [Mg/Fe] (only the second precise [Mg/Fe] measurement at z~2), iron abundance [Fe/H] and, for the first time, their spatial gradient, as well as the stellar abundances of 6 other elements using full spectral fitting. I finally present my analysis of the other four galaxies in this sample where in addition to investigating their chemical abundances, I study their star formation histories reconstructed by fitting synthetic models to their multi-band photometric and spectroscopic data. I show these galaxies have significant Mg-enhancement which cannot be explained solely by short formation timescale as proposed by simple chemical evolution models, then I discuss new scenarios to explain chemical abundances in these galaxies.

Mathematical models used in preclinical drug discovery tend to be empirical growth laws. Such models are well suited to fitting the data available, mostly longitudinal studies of tumor volume; however, they typically have little connection with the underlying physiologic processes. This lack of a mechanistic underpinning restricts their flexibility and potentially inhibits their translation across studies including from animal to human. Here we present a mathematical model describing tumor growth for the evaluation of single-agent cytotoxic compounds that is based on mechanistic principles. The model can predict spatial distributions of cell subpopulations and account for spatial drug distribution effects within tumors. Importantly, we demonstrate that the model can be reduced to a growth law similar in form to the ones currently implemented in pharmaceutical drug development for preclinical trials so that it can integrated into the current workflow. We validate this approach for both cell-derived xenograft and patient-derived xenograft (PDX) data. This shows that our theoretical model fits as well as the best performing and most widely used models. However, in addition, the model is also able to accurately predict the observed growing fraction of tumours. Our work opens up current preclinical modeling studies to also incorporating spatially resolved and multimodal data without significant added complexity and creates the opportunity to improve translation and tumor response predictions. Significance: This theoretical model has the same mathematical structure as that currently used for drug development. However, its mechanistic basis enables prediction of growing fraction and spatial variations in drug distribution.

Objective Stroke survivors commonly suffer from dysphagia, originating from oro-facial impairments which affect swallowing function. Functional therapy often employs tongue exercises that require the patient to perform short motion sequences. Evaluating the patients performance on those exercises is difficult, because there is no reliable form of visual feedback. Methods We propose an optopalatographic device that does not require a personalized dental retainer and is capable of measuring tongue movement trajectories intraorally. The device features nine optical proximity sensors at 100 Hz and is fixated against the hard palate with a specifically developed palatal adhesive. The sensing capabilities of the device were evaluated on a tongue gesture corpus recorded from nine healthy individuals, containing eight different tongue exercises commonly used in functional dysphagia therapy. Results The measured tongue trajectories contained temporally and spatially resolved information about the tongue movement and location during each exercise. Furthermore, a simple DTW-kNN classifier was able to distinguish the exercises from one another with an average classification accuracy of 97.9 % and 61.4 % (cross-validation and inter-speaker test accuracy, respectively). Conclusion the device can provide real-time feedback for tongue motion and we obtained promising gesture recognition results with relatively few sensors, even in the absence of a personalized dental retainer. Significance Non-personalized optopalatography is readily available and could aid in improving functional dysphagia therapy by providing visual feedback to both the physician and patient.

Aggregation of the tau protein plays a central role in several neurodegenerative diseases collectively known as tauopathies, including Alzheimer’s and Parkinson’s disease. Tau misfolds into fibrillar beta sheet structures that constitute the paired helical filaments found in Neurofibrillary tangles. It is known that there can be significant structural heterogeneities in tau aggregates associated with different diseases. However, while structures of mature fibrils have been studied, the structural distributions in early stage tau aggregates is not well understood. In the present study, we use AFM-IR to investigate nanoscale spectra of individual tau fibrils at different stages of aggregation and demonstrate the presence of multiple fibrillar polymorphs that exhibit different secondary structures. We further show that mature fibrils contain significant amounts of antiparallel beta sheets. Our results are the very first application of nanoscale infrared spectroscopy to tau aggregates and underscore the promise of spatially resolved infrared spectroscopy for investigating protein aggregation.

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

H$\alpha$ blobs are off-galaxy emission-line regions with weak or no optical counterparts. They are mostly visible in H$\alpha$ line, appearing as concentrated blobs. Such unusual objects have been rarely observed and studied, and their physical origin is still unclear. We have identified 13 H$\alpha$ blobs in the public data of MaNGA survey, by visually inspecting both the optical images and the spatially resolved maps of H$\alpha$ line for $\sim 4600$ galaxy systems. Among the 13 H$\alpha$ blobs, 2 were reported in previously MaNGA-based studies and 11 are newly discovered. This sample, though still small in size, is by far the largest sample with both deep imaging and integral field spectroscopy. Therefore, for the first time we are able to perform statistical studies to investigate the physical origin of H$\alpha$ blobs. We examine the physical properties of these H$\alpha$ blobs and their associated galaxies, including their morphology, environments, gas-phase metallicity, kinematics of ionized gas, and ionizing sources. We find that the H$\alpha$ blobs in our sample can be broadly divided into two groups. One is associated with interacting/merging galaxy systems, of which the ionization is dominated by shocks or diffuse ionized gas. It is likely that these H$\alpha$ blobs used to be part of their nearby galaxies, but were stripped away at some point due to tidal interactions. The other group is found in gas-rich systems, appearing as low-metallicity star-forming regions that are visually detached from the main galaxy. These H$\alpha$ blobs could be associated with faint disks, spiral arms, or dwarf galaxies., Comment: 26 pages, 18 figures, accepted for publication in MNRAS

Spatial metabolomics can reveal intercellular heterogeneity and tissue organization. Here we report on the spatial single nuclear metabolomics (SEAM) method, a flexible platform combining high-spatial-resolution imaging mass spectrometry and a set of computational algorithms that can display multiscale and multicolor tissue tomography together with identification and clustering of single nuclei by their in situ metabolic fingerprints. We first applied SEAM to a range of wild-type mouse tissues, then delineated a consistent pattern of metabolic zonation in mouse liver. We further studied the spatial metabolic profile in the human fibrotic liver. We discovered subpopulations of hepatocytes with special metabolic features associated with their proximity to the fibrotic niche, and validated this finding by spatial transcriptomics with Geo-seq. These demonstrations highlighted SEAM’s ability to explore the spatial metabolic profile and tissue histology at the single-cell level, leading to a deeper understanding of tissue metabolic organization. SEAM is a platform for the analysis of high-resolution secondary ion mass spectrometry imaging that allows spatially resolved nuclear metabolomic profiling at the single-cell level.

Three-dimensional cell cultures are of growing importance in biochemical research as they represent tissue features more accurately than standard two-dimensional systems, but to investigate these challenging new models an adaptation of established analytical techniques is required. Spatially resolved data for living organoids are needed to gain insight into transport processes and biochemical characteristics of domains with different nutrient supply and waste product removal. Within this work, we present an NMR-based approach to obtain dynamically radial metabolite profiles for cell spheroids, one of the most frequently used 3D models. Our approach combines an easy to reproduce custom-made measurement design, maintaining physiological conditions without inhibition of the NMR experiment, with spatially selective NMR pulse sequences. To overcome the inherently low sensitivity of NMR spectroscopy we excited slices instead of smaller cube-like voxels in combination with an efficient interleaved measurement approach and employed a commercially available cryogenic NMR probe. Finally, radial metabolite profiles could be obtained via double Abel inversion of the measured one-dimensional intensity profiles. Applying this method to Ty82 cancer cell spheroids demonstrates the achieved spatial resolution, for instance confirming exceedingly high lactic acid and strongly decreased glucose concentrations in the oxygen-depleted core of the spheroid. Furthermore, our approach can be employed to investigate fast and slow metabolic changes in single spheroids simultaneously, which is shown as an example of a spheroid degrading over several days after stopping the nutrient supply.

Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated computational challenges as well as highlight the opportunities for standardized benchmarking metrics and data-sharing infrastructure in spurring innovation moving forward.

Assessment of exposure and mortality attributable to air pollution is one of the most important tasks to inform strategies for mitigation and prevention of the negative effects of degraded air quality. While the vast majority of studies rely on either satellite data or sparse (and expensive) monitoring networks and focus on global- or regional-scale assessments, emerging low-cost citizen science networks are a new source of information that can be used to provide more detailed estimates at urban scale, thereby refining previous estimates and providing spatially resolved information at census tract resolution. In this work, we demonstrate how a citizen science network, validated with a reference air pollution station, provides sensibly different (and spatially resolved) estimates of air pollution levels and associated exposure/mortality than assuming constant air pollution across the analyzed urban area. Specifically we focus on the extreme pollution episode of the 2020 California wildfires that significantly impacted the city of San Francisco. We estimate that during the wildfires, degraded air quality was responsible for an increase between 2 and 4% of the baseline mortality in the same city over the time period of study (August 19, 2020 to September 30, 2020).

Spatially varying diesel locomotive fuel use and emission rates (FUERs) are needed to accurately quantify local emission hotspots and their health impacts. However, existing locomotive FUER data are typically not spatially resolved or representative of real-world locomotive operation. Therefore, existing data are of limited use in quantifying the spatial variability in real-world FUERs. The objectives of this work are to quantify spatial variability in locomotive FUERs and identify factors differentiating hotspots from non-hotspots. FUERs were measured based on real-world measurements conducted for the Piedmont passenger rail service using a portable emission measurement system. FUERs were quantified based on 0.25 mile track segments on the Piedmont route. Hotspots were defined as segments in the top quintile of segment-average FUERs. On average, hotspots contributed 40-50% to trip fuel use and emissions. Hotspots were typically associated with low-to-medium speed, and high acceleration and grade. In contrast, non-hotspots were associated with high speed, and low acceleration and grade. Hotspots were typically located near populated areas and, thus, may exacerbate air pollutant exposure. The method demonstrated here can be applied to other passenger train services to assess key trends in hotspot locations and factors that explain the occurrence of hotspots.

In the current era of global climate change, environmental stresses, especially drought and salt, have impaired the growth and productivity of crops, e.g., cotton. Understanding the mechanisms of plants' adaptation to these abiotic stresses is crucial to breed stress-tolerant crop species. In the present study, integrated metabolomics, lipidomics, and mass spectrometry imaging (MSI) were used to discover the spatial distribution of differential metabolites and lipids in two cottonseed cultivars with contrasting drought and salt tolerance properties. Seventeen differential metabolites and 125 differential lipids were identified. Their possible roles in augmenting stress tolerance were illustrated, which were involved in reactive oxygen species scavenging, osmotic adjustment, and cell membrane structure reconstruction. MSI analysis provided a visualization of nine differential lipids and four differential metabolites in cottonseeds with varied abundances and distributions. The results may help understand cottonseeds' convictive metabolic and lipidomic regulatory networks in coping with salinity and drought stresses and give new insights into the stress-tolerance traits relevant to other crops.

The conventional microbial cell analyses are mostly population-averaged methods that conceal the characteristics of single-cell in the community. Single-cell analysis can provide information on the functional and structural variation of each cell, resulting in the elimination of long and tedious microbial cultivation techniques. Raman spectroscopy is a label-free, noninvasive, and in-vivo method ideal for single-cell measurement to obtain spatially resolved chemical information. In the current review, recent developments in Raman spectroscopic techniques for microbial characterization at the single-cell level are presented, focusing on Raman imaging of single cells to study the intracellular distribution of different components. The review also discusses the limitation and challenges of each technique and put forward some future outlook for improving Raman spectroscopy-based techniques for single-cell analysis. Raman spectroscopic methods at the single-cell level have potential in precision measurements, metabolic analysis, antibiotic susceptibility testing, resuscitation capability, and correlating phenotypic information to genomics for cells, the integration of Raman spectroscopy with other techniques such as microfluidics, stable isotope probing (SIP), and atomic force microscope can further improve the resolution and provide extensive information. Future focuses should be given to advance algorithms for data analysis, standardized reference libraries, and automated cell isolation techniques in future.

Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artefacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated dataset for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.1% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.5%. Next, we used the cell classification results to search for cell niches with an unsupervised learning approach based on graph neural networks. We show that the approach can re-detect specialized tissue niches in previously published data, and that our proposed cell classification leads to niche definitions that may be relevant for sub-groups of glioma, if applied to larger datasets. This article is protected by copyright. All rights reserved.

Polymer brush surfaces that alter their physical properties in response to chemical stimuli have the capacity to be used as new surface‐based sensing materials. For such surfaces, detecting the polymer conformation is key to their sensing capabilities. Herein, we report on FRET‐integrated ultrathin (, FRET chemistry was integrated within stimuli‐responsive polymer brush layers on planar substrates for spatial sensing of changing polymer conformations. Sensing was demonstrated for a variety of liquid mixture compositions, including high‐resolution observation of lateral differences in polymer conformation at immiscible liquid interfaces (see picture), thus offering great potential to be leveraged for new surface‐based sensing devices.

Although visible and near-infrared (Vis-NIR) spectroscopy can rapidly and nondestructively identify wood species, the conventional spectrometer approach relies on the aggregate light absorption due to the chemical composition of wood and light scattering originating from the physical structure of wood. Hence, much of the work in this area is still limited to further spectral pretreatments, such as baseline correction and standard normal variate to reduce the light scattering effects. However, it should be emphasized that the light scattering rather than absorption in wood is dominant, and this must be effectively utilized to achieve highly accurate and robust wood classification. Here a novel method based on spatially resolved diffuse reflectance (wavelength range: 600–1000 nm) was demonstrated to classify 15 kinds of wood. A portable Vis-NIR spectral measurement system was designed according to previous simulations and experimental results. To simplify spectral data analysis (i.e., against overfitting), support vector machine (SVM) model was constructed for wood sample classification using principal component analysis (PCA) scores. The classification accuracies of 98.6% for five-fold cross-validation and 91.2% for test set validation were achieved. This study offers enhanced classification accuracy and robustness over other conventional nondestructive approaches for such various kinds of wood and sheds light on utilizing visible and short-wave NIR light scattering for wood classification.

Recent technological advances have enabled spatially resolved measurements of expression profiles for hundreds to thousands of genes in fixed tissues at single-cell resolution. However, scalable computational analysis methods able to take into consideration the inherent 3D spatial organization of cell types and nonuniform cellular densities within tissues are still lacking. To address this, we developed MERINGUE, a computational framework based on spatial autocorrelation and cross-correlation analysis to identify genes with spatially heterogeneous expression patterns, infer putative cell–cell communication, and perform spatially informed cell clustering in 2D and 3D in a density-agnostic manner using spatially resolved transcriptomic data. We applied MERINGUE to a variety of spatially resolved transcriptomic data sets including multiplexed error-robust fluorescence in situ hybridization (MERFISH), spatial transcriptomics, Slide-seq, and aligned in situ hybridization (ISH) data. We anticipate that such statistical analysis of spatially resolved transcriptomic data will facilitate our understanding of the interplay between cell state and spatial organization in tissue development and disease.

Optical imaging probes allow us to detect and uncover the physiological and pathological functions of an analyte of interest at the molecular level in a non-invasive, longitudinal manner. By virtue of simplicity, low cost, high sensitivity, adaptation to automated analysis, capacity for spatially resolved imaging and diverse signal output modes, optical imaging probes have been widely applied in biology, physiology, pharmacology and medicine. To build a reliable and practically/clinically relevant probe, the design process often encompasses multidisciplinary themes, including chemistry, biology and medicine. Within the repertoire of probes, dual-locked systems are particularly interesting as a result of their ability to offer enhanced specificity and multiplex detection. In addition, chemiluminescence is a low-background, excitation-free optical modality and, thus, can be integrated into dual-locked systems, permitting crosstalk-free fluorescent and chemiluminescent detection of two distinct biomarkers. For many researchers, these dual-locked systems remain a ‘black box’. Therefore, this Review aims to offer a ‘beginner’s guide’ to such dual-locked systems, providing simple explanations on how they work, what they can do and where they have been applied, in order to help readers develop a deeper understanding of this rich area of research. Dual-locked optical probes change their optical signals when they respond to two biomarkers of interest. This facilitates real-time imaging of multiple interrelated biomarkers in living systems and, thus, provides opportunities to better understand pathological events and enhanced diagnostic specificity.

Aleksandr Il’ich Glazunov developed the first electrochemical technique to image the surface of conducting solids giving the technique the name electrography. The electrographic images can mirror the distribution of elements on the surface of solid materials and also the electrochemical activity, caused by variations of “dissolution tension”. Thus, he has established for the first time a kind of spatially resolved electrochemistry. Electrography is also the first direct electroanalytical technique for solid materials. The present paper gives an account of his turbulent life in Russia, Czechoslovakia and Chile, and a discussion of his main scientific achievement, the development of electrography.

The success of luminescence dating as a chronological tool in Quaternary science builds upon innovative methodological approaches, providing new insights into past landscapes. Infrared radiofluorescence (IR-RF) on K-feldspar is such an innovative method that was already introduced two decades ago. IR-RF promises considerable extended temporal range and a simple measurement protocol, with more dating applications being published recently. To date, all applications have used multi-grain measurements. Herein, we take the next step by enabling IR-RF measurements on a single grain level. Our contribution introduces spatially resolved infrared radiofluorescence (SR IR-RF) on K-feldspars and intends to make SR IR-RF broadly accessible as a geochronological tool. In the first part of the article, we detail equipment, CCD camera settings and software needed to perform and analyse SR IR-RF measurements. We use a newly developed ImageJ macro to process the image data, identify IR-RF emitting grains and obtain single-grain IR-RF signal curves. For subsequent analysis, we apply the statistical programming environment R and the package Luminescence. In the second part of the article, we test SR IR-RF on two K-feldspar samples. One sample was irradiated artificially; the other sample received a natural dose. The artificially irradiated sample renders results indistinguishable from conventional IR-RF measurements with the photomultiplier tube. The natural sample seems to overestimate the expected dose by ca. 50 % on average. However, it also shows a lower dose component, resulting in ages consistent with the same sample's quartz fraction. Our experiments also revealed an unstable signal background due to our cameras' degenerated cooling system. Besides this technical issue specific to the system we used, SR IR-RF is ready for application. Our contribution provides guidance and software tools for methodological and applied luminescence (dating) studies on single-grain feldspars using radiofluorescence.

The developing roles of damage mechanisms in the failure response of SiC/SiC minicomposites was investigated by the characterization of microscale damage accumulation with respect to microstructure. A multi-modal approach combining spatially resolved acoustic emission (AE) with tensile testing in-SEM (scanning electron microscope) was used to simultaneously examine surface (observed in-SEM) and bulk damage (monitored via AE). Strong agreement was shown between the evolving crack density estimated by AE and in-SEM measurements. The following were observed: (i) in-plane matrix content and distribution impacted crack growth; (ii) spatially-distributed matrix cracks generated varying stress-dependent AE; and (iii) certain individual cracks became more probable failure locations due to unique combinations of damage mechanisms that drove their growth. This approach enabled characterizing potential failure determinants and suggests that early damage behavior is related to certain microstructural features (e.g. surface flaws), while subsequent damage behavior is coupled to interactions of local mechanisms evolving with stress.

Glacial lakes dynamics has a concomitant impact over glacial retreat, mass change and glacial lake outburst floods (GLOFs) events. Recent warming led increase in glacial retreat at High-Mountain Asia (HMA) which has accelerated the formation of moraine-dammed glacial lakes; however, their relative potential for catastrophic events i.e., GLOF, are still a matter of detailed research. In the present study, the changes are assessed at the terminus position, glacier area and associated proglacial lake of Gepang Gath Glacier (GGGL), Chandra basin, western Himalaya, India, using multi-years satellite dataset of Landsat series (TM, ETM+ and OLI, 1989–2017). The results of glacial volume estimations using volume-area scaling method suggests ∼0.28 km3 of ice volume loss between 1989 and 2017. The glacier has retreated ∼846 m with an average rate of 30 m a−1 and lost ∼0.73 ± 0.05 km2 frontal area from 1989 to 2017. However, the glacier has not shown uniform retreat rate. Between 1989 and 2000 glacier retreat about ∼14 m a−1 and the rate was increased gradually to ∼38 m a−1 (2000–2009) and ∼50 m a−1 (2015–2017). The calculated proglacial lake area has expanded ∼44 % and estimated equilibrium line altitude (ELA) rise was 54 ± 12 m, during the study period. The study explains that the continuous expansion of proglacial lake and ice loss can be dangerous to downstream region. It is concluded by underlining the importance of glacier-lake relationships to predict the glacial lake behavior and understand the probability of lake catastrophic events i.e., GLOF and associated hazards.